Monthly Archives: November 2013

Measurement of Neuromuscular Performance Capacities

Neuromuscular Functional Units Purposes of Measuring Selected Neuromuscular Performance Capacities

Range of Motion and Extremes of Motion Movement Terminology • Factors Influencing ROM/EOM and ROM/EOM Measurements • Instrumented Systems Used to Measure ROM/EOM • Key Concepts in Goniometric Measurement • Numerical Notation Systems


Strength Testing and Muscle Terminology • Factors Influencing Muscle Strength and Strength Measurement • Grading Systems and Parameters Measured • Methods and Instruments Used to Measure Muscle Strength • Key Concepts in Measuring Strength

Speed of Movement

Speed of Movement Terminology • Factors Influencing Speed of Movement and Speed of Movement Measurements • Parameters Measured • Instruments Used to Measure Speed of Movement • Key Concepts in Speed of Movement Measurement


Endurance Terminology • Factors Influencing Neuromuscular Endurance and Measurement of Endurance • Parameters Measured • Methods and Instruments Used to Measure Neuromuscular Endurance • Key Concepts in Measuring Muscle Endurance

Susan S. Smith

Texas Woman’s University

подпись: susan s. smith
texas woman’s university
Reliability, Validity, and Limitations in Testing

Performance Capacity Space Representations


Movements allow us to interact with our environment, express ourselves, and communicate with each other. Life is movement. Movement is constantly occurring at many hierarchical levels including cellular and subcellular levels. By using the adjective “human” to clarify the term “movement”, we are not only defining the species of interest, but also limiting the study to observable performance and its more overt causes. Study of human performance is of interest to a broad range of professionals including rehabili­tation engineers, orthopaedic surgeons, therapists, biomechanists, kinesiologists, psychologists, and so
On. Because of the complexity of human performance and the variety of investigators, the study of human performance is conducted from several theoretical perspectives including, (1) anatomical, (2) purpose or character of the movement (such as locomotion), (3) physiological, (4) biomechanical, (5) psychological, (6) socio-cultural, and (7) integrative. The Elemental Resource Model (ERM), pre­sented at the beginning of this section, is an integrative model which incorporates aspects of the other models into a singular system accounting for the human, the task, and the human-task interface [Kon­draske, 1999].

The purposes of this chapter are to: (1) provide reasons for measuring four selected variables of human performance: extremes/range of motion, strength, speed of movement, and endurance; (2) briefly define and discuss these variables; (3) overview selected instruments and methods used to measure these vari­ables; and (4) discuss interpretation of performance for a given neuromuscular subsystem.

Neuromuscular Functional Units

While the theoretical perspectives listed previously may be useful within specific contexts or within specific disciplines, the broader appreciation of human performance and its control can be gained from the perspective of an integrative model such as the ERM [Kondraske, 1999]. This model organizes performance resources into four different domains. Basic movements, such as elbow flexion, are executed by neuromuscular functional units in the environmental interface domain. Intermediate and complex tasks, such as walking and playing the piano, utilize multiple basic functional units. A person performing a movement operates the involved functional units along different dimensions of performance according to the demands of the task. Dimensions of performance are factors such as joint motion, strength, speed of movement, and endurance. Lifting a heavy box off the floor requires, among other things, a specific amount of strength associated with neuromuscular functional units of the back, legs, and arms according to the weight and size of the box. Reaching for a light weight box from the top shelf of a closet requires that the shoulder achieve certain extremes of motion according to the height of the shelf.

Whereas four dimensions of performance are considered individually in this chapter, they are highly interdependent. For an example, strength availability during a movement is partly dependent on joint angle. Despite interdependence, considering the variables as different dimensions is essential to studying human performance. The components limiting a person’s ability to complete a task can only be identified and subsequently enhanced by determining, for example, that the reason a person cannot reach the box off the top shelf is not because of insufficient range of motion of the shoulder, but because of insufficient strength of the shoulder musculature required to lift the arm through the range of motion. Isolating the subsystems involved in a task and maximally stressing them along one or more “isolated” dimensions of performance is a key concept in the ERM. “Maximally stressing” the subsystems means that the maximum amount of the resource available is being determined. This differs from determining the amount of the resource which happened to be used while performing a particular task. Often the distinction between obtaining maximal performance vs. submaximal performance is in the instructions given to the subject. For example, in measuring speed, we say “move as fast as you can.”

Purposes of Measuring Selected Neuromuscular Performance Capacities

Range of motion, strength, speed of movement, and/or endurance can be measured for one or more of the following purposes:

To determine the amount of the resource available and to compare it to the normal value for that individual. “Normal” is frequently determined by comparisons with the opposite extremity or with normative data when available. This information may be used to develop goals and a program to change the performance.

To assist in determining the possible effects of insufficient or imbalanced amounts of the variable on a person’s performance of activities of daily living, work, sport, and leisure pursuits. In this Case, the amount of the variable is compared to the demands of the task, rather than to norms or to the opposite extremity.

To assist in diagnosis of medical conditions and the nature of movement dysfunctions.

To reassess status in order to determine the effectiveness of a program designed to change the amount of the variable.

To motivate persons to comply with treatment or training regimes.

To document status and the results of treatment or training and to communicate with other involved persons.

To assist in ergonomically designed furniture, equipment, techniques, and environments.

To provide information to combine with other measures of human performance to predict func­tional capabilities.

Range of Motion and Extremes of Motion

Range of motion (ROM) is the amount of movement that occurs at a joint. Range of motion is typically measured by noting the extremes of motion (EOM). The designated reference or zero position must be specified for measurements of the two extremes of motion. For example, to measure elbow (radiohumeral joint) flexion and extension, the preferred starting position is with the subject supine with the arm parallel to the lateral midline of the body with the palm facing upward [Norkin and White, 1995; Palmer and Epler, 1998]. Measurements are taken with the elbow in the fully flexed position and with the elbow in the fully extended position.

Movement Terminology

Joint movements are described using a coordinate system with the human body in an anatomical position. The anatomical position of the body is an erect position, face forward, arms at sides, palms facing forward, and fingers and thumbs in extension. The central coordinate system consists of three cardinal planes and axes with its origin located between the cornua of the sacrum [Panjabi, White, and Brand, 1974]. Figure 148.1 demonstrates the planes and axes of the central coordinate system. The same coordinate system can parallel the master system at any joint in the body by relocating the origin to any defined point.

The sagittal plane is the y, z plane; the frontal (or coronal) plane is the y, x plane; the horizontal (or transverse) plane is the x, z plane. Movements are described in relation to the origin of the coordinate system. The arrows indicate the positive direction of each axis. An anterior translation is +z; a posterior translation is – z. Clockwise rotations are +□, and counterclockwise rotations are – Q

Joints are described as having degrees of freedom (dof) of movement. Dof is the number of indepen­dent coordinates in a system that are necessary to accurately specify the position of an object in space. If a motion occurs in one plane and around one axis, the joint is defined as having one dof. Joints with movements in two planes occurring around two different axes, have two dof, and so on.

Angular movements refer to motions that cause an increase or decrease in the angle between the articulating bones. Angular movements are flexion, extension, abduction, adduction, and lateral flexion (See Table 148.1). Rotational movements generally occur around a longitudinal (or vertical) axis except for movements of the clavicle and scapula. The rotational movements occurring around the longitudinal axis (internal rotation, external rotation, opposition, horizontal abduction, and horizontal adduction) are described in Table 148.1. Rotation of the scapula is described in terms of the direction of the inferior angle. Movement of the inferior angle of the scapula toward the midline is a medial (or downward) rotation, and movement of the inferior angle away from the midline is lateral (or upward) rotation. In the extremities the anterior surface of the extremity is used as the reference area. Because the head, neck, trunk, and pelvis rotate about a midsagittal, longitudinal axis, rotation of these parts is designated as right or left. As can be determined from Fig. 148.1, Axial rotation of the trunk toward the left can be described mathematically as +Qy.

A communications problem often exists in describing motion using the terms defined in Table 148.1. A body segment can be in a position such as flexion, but can be moving toward extension. This confusion is partially remedied by using the form of the word with the suffix,

FIGURE 148.1 Planes and axes are illustrated in anatomical position. The central coordinate system with its origin between the cornua of the sacrum is shown. Source: White III AA, Panjabi MM. 1990. Clinical Biomechanics of the Spine, 2nd ed., p 87. Philadelphia, JB Lippincott Com­pany. With permission.

figure 148.1 planes and axes are illustrated in anatomical position. the central coordinate system with its origin between the cornua of the sacrum is shown. source: white iii aa, panjabi mm. 1990. clinical biomechanics of the spine, 2nd ed., p 87. philadelphia, jb lippincott company. with permission.
-ion, to indicate a static position and using the suffix, – ing, to denote a movement. Thus, an elbow can be in a posi­tion of 90D flexion and also extending.

Factors Influencing ROM/EOM and ROM/EOM Measurements

The ROM and EOM available at a joint is determined by morphology and the soft tissues surrounding and cross­ing a joint, including the joint capsule, ligaments, ten­dons, and muscles. Other factors such as age, gender, swelling, muscle mass development, body fat, passive insufficiency (change in the ROM/EOM available at one joint in a two-joint muscle complex caused by the posi­tion of the other joint), and time of day (diurnal effect) also affect the amount of motion available. Some persons, because of posture, genetics, body type, or movement habits, normally demonstrate hypermobile or hypomo­bile joints. Dominance has not been found to significantly affect available ROM. See discussion in Miller [1985]. The shapes of joint surfaces, which are designed to allow movement in particular directions, can become altered by disease, trauma, and posture, thereby, increasing or decreasing the ROM/EOM. Additionally, the soft tissues crossing a joint can become tight (contracted) or over­stretched altering the ROM/EOM.

The type of movement, active or passive, also affects ROM/EOM. When measuring active ROM (AROM), the person voluntarily contracts muscles and moves the body part through the available motion. When measuring passive ROM (PROM), the examiner moves the body part through the ROM. PROM is usually slightly greater than AROM due to the extensibility of the tissues crossing and comprising the joint. AROM can be decreased because of restricted joint mobility, muscle weakness, pain, unwillingness to move, or inability to follow instructions. PROM is assessed to determine the integrity of the joint and the extent of structural limitation.

Instrumented Systems Used to Measure ROM/EOM

The most common instrument used to measure joint ROM/EOM is a goniometer. The universal goni­ometer, shown in Fig. 148.2a, iS most widely used clinically. A variety of universal goniometers have been developed for specific applications. Two other types of goniometers are also shown in Fig. 148.2.

Table 148.2 lists and compares several goniometric instruments used to measure ROM/EOM. Choice of the instrument used to measure ROM/EOM depends upon the degree of accuracy required, time available to the examiner, the measurement environment, the body segment being measured, and the equipment available.

Non-goniometric methods of joint measurement are available. Tape measures, radiographs, photo­graphy, cinematography, videotape, and various optoelectric movement monitoring systems can also be

Movement Term



Description of Movement




Bending of a part such that the anterior surfaces approximate each other. However, flexion of the knee, ankle, foot, and toes refers to movement in the posterior direction.




Opposite of flexion; involves straightening a body part.




Movement away from the midline of the body or body part; abduction of the wrist is sometimes called radial deviation.




Movement towards the midline of the body or body part; adduction of the wrist is sometimes called ulnar deviation.

Lateral flexion



Term used to denote lateral movements of the head, neck, and trunk.

Internal (medial)



Turning movement of the anterior surface of a part towards the midline of


The body; internal rotation of the forearm is referred to as pronation.

External (lateral)



Turning movement of the anterior surface of a part away from the midline


Of the body; external rotation of the forearm is referred to as supination.




Movement of the tips of the thumb and little finger toward each other.




Movement of the arm in a posterior direction away from the midline of the


Body with the shoulder joint in 90D of either flexion or abduction.




Movement of the arm in an anterior direction toward the midline of the





Depends on

Term used to describe certain movements of the scapula and pelvis. In the

On joint


Scapula, an anterior tilt occurs when the coracoid process moves in an anterior and downward direction while the inferior angle moves in a posterior and upward direction. A posterior tilt of the scapula is the opposite of an anterior tilt. In the pelvis, an anterior tilt is rotation of the anterior superior spines (ASISs) of the pelvis in an anterior and downward direction; a posterior tilt is movement of the ASISs in a posterior and upward direction. A lateral tilt of the pelvis occurs when the pelvis is not level from side to side, but one ASIS is higher than the other one.



Depends on

Movements that occur when one articulating surface slides on the opposite

On joint





A gliding movement of the scapula in an upward direction as in shrugging the shoulders.



Movement of the scapula downward in a direction reverse of elevation.

Used to measure or calculate the motion available at various joints. These methods are beyond the scope of this chapter.

Key Concepts in Goniometric Measurement

Numerous textbooks [Clarkson and Gilewich, 1989; Norkin and White, 1995; Palmer and Epler, 1998] are available that describe precise procedures for goniometric measurements of each joint. Unfortunately, there is a lack of standardization among these references.

In general, the anatomical position of zero degrees (preferred starting position) is the desired starting position for all ROM/EOM measurements except rotation at the hip, shoulder, and forearm. The arms of the goniometer are usually aligned parallel and lateral to the long axis of the moving and the fixed body segments in line with the appropriate landmarks. In the past, some authors contended that place­ment of the axis of the goniometer should be congruent with the joint axis for accurate measurement [West, 1945; Wiechec and Krusen, 1939]. However, the axis of rotation for joints changes as the body segment moves through its ROM; therefore, a goniometer cannot be placed in a position in line with the joint axis during movement. Robson [1966] described how variations in the placement of the goniometer’s axis could affect the accuracy of ROM measurements. Miller [1985] suggested that the axis problem could be handled by ignoring the goniometer’s axis and concentrating on the accurate alignment of the arms of the goniometer with the specified landmarks. Potentially some accuracy may be sacrificed,

Measurement of Neuromuscular Performance Capacities

FIGURE 148.2 Three types of goniometric instruments used to measure range and extremes of motion are shown: (a) typical 180- and 360-degree universal goniometers of various sizes; (b) a fluid goniometer, which is activated by the effects of gravity; (c) an APM I digital electronic device that works similarly to a pendulum goniometer.

подпись: figure 148.2 three types of goniometric instruments used to measure range and extremes of motion are shown: (a) typical 180- and 360-degree universal goniometers of various sizes; (b) a fluid goniometer, which is activated by the effects of gravity; (c) an apm i digital electronic device that works similarly to a pendulum goniometer.

(b) (c)

подпись: (b) (c)But the technique is simplified and theoretically more reproducible. The subject’s movement is observed for unwanted motions that could result in inaccurate measurement. For example, a subject might attempt to increase forearm supination by laterally flexing the trunk.

Universal goniometer

A protractor-like device with one arm considered movable and the other arm stationary; protractor can have a 180Dor 360Dscale and is usually numbered in both directions; available in a range of sizes and styles to accommodate different joints (See Fig. 148.2a).

Fluid (or bubble) goniometer

A device with a fluid-filled channel with a 360°—scale that relies on the effects of gravity (See Fig. 148.2b); dial turns allowing the goniometer to be “zeroed;” some models are strapped on and others must be held against the body part.

Pendulum goniometer

A scaled, inclinometer-like device with a needle or pointer (usually weighted); some models are strapped on and others must be held against the body part (not shown).

“Myrin” OB goniometer

A fluid-filled, rotatable container consisting of compass needle that responds to the earth’s magnetic field (to measure horizontal motion), a gravity-activated inclination needle (to measure frontal and sagittal motion), and a scale (not shown).

Arthrodial protractor

A large, flat, clear plastic protractor without arms that has a level on the straight edge (not shown). APM I

Computerized goniometer with digital sensing and electronics; can perform either continuous monitoring or calculate individual ROM/EOM from a compound motion function (See Fig. 148.2c).


Arms of a goniometer are attached to a potentiometer and are strapped to the proximal and distal body parts; movement from the device causes resistance in the potentiometer which measures the ROM (not shown).

Inexpensive; portable; familiar devices; size of the joint being measured determines size of the goniometer used; clear plastic goniometers have a line through the center of the arms to make alignment easier and more accurate; finger goniometers can be placed over the dorsal aspect of the joint being measured.

Quick and easy to use because it is not usually aligned with bony landmarks; does not have to conform to body segments; useful for measuring neck and spinal movements; using a pair of fluid goniometers permits distinguishing regional spinal motion.

Inexpensive; same advantages as for the fluid goniometer described above.

Can be strapped on the body part allowing the hands free to stabilize and move the body part; not necessary to align the goniometer with the joint axis; permits measurements in all three planes.

Does not need to conform to body segments; most useful for measuring joint rotation and axioskeletal motion.

Easy to use; provides rapid digital read­out; measures angles in any plane of motion; one hand is free to stabilize and move body segments; particularly easy for measuring regional spinal movements.

More useful for dynamic ROM, especially for determining kinematic variables during activities such as gait; provides immediate data; some electrogoniometers permit measurement in one, two, or three dimensions.

Several goniometers of different sizes may be required, especially if digits are measured; full – circle models may be difficult to align when the subject is recumbent and axis alignment is inhibited by the protractor bumping the surface; the increments on the protractors may vary from 1Q 2Q or 5Q placement of the arms is a potential source of error.

More expensive than universal goniometers; using a pair of goniometers is awkward; useless for motions in the horizontal plane; error can be induced by slipping, skin movement, variations in amount of soft tissue owing to muscle contraction, swelling, or fat, and the examiner’s hand pressure changing body segment contour; reliability may be sacrificed from lack of orientation to landmarks and difficulty with consistent realignment [Miller, 1985].

Some models cannot be “zeroed”; useless for motions in the horizontal plane; same soft tissue error concerns as described above for the fluid goniometer.

Expensive and bulky compared with universal goniometer; not useful for measuring small joints of hand and foot; susceptible to magnetic fields [Clarkson and Gilewich, 1989]; subject to same soft tissue error concerns described above under fluid goniometer.

Not useful for measuring smaller joints, especially those with lesser ROMs; usually scaled in large increments only.

Expensive compared to most other instruments described; device must be rotated perpendicular to the direction of segment motion only; unit must stabilize prior to measurement; excessive delays in recording must be avoided; subject to the same soft tissue error concerns as described above under fluid goniometer.

Aligning and attaching the device is time­consuming and not amenable to all body segments; device and equipment needed to use it are moderately expensive; essentially laboratory equipment; less accurate for measurement of absolute limb position; device itself is cumbersome and may alter the movement being studied.

Numerical Notation Systems

Three primary systems exist for expressing joint motion in terms of degrees. These are the 0-180 System, the 180-0 System, and the 360 System. The 0-180 System is the most widely accepted system in medical applications and may be the easiest system to interpret. In the 0-180 System, the starting position for all movements is considered to be 00 and movements proceed toward 1800 As the joint motion increases, the numbers on the goniometric scale increase. In the 180-0 System, movements toward flexion approach 00 and movements toward extension approach 1800 Different rules are used for the other planes of motion. The 360 System is similar to the 180-0 System. In the 360 System, movements are frequently performed from a starting position of 1800 Movements of extension or adduction which go beyond the neutral position approach 3600 Joint motion can be reported in tables, charts, graphs, or pictures. In the 0-180 System, the starting and ending ranges are recorded separately, as 0G-130Q If a joint cannot be started in the 00 position, the actual starting position is recorded, as 10G-130Q


Muscle strength implies the force or torque production capacity of muscles. However, to measure strength, the term must be operationally defined. One definition modified from Clarkson and Gilewich [1989] states that muscular strength is the maximal amount of torque or force that a muscle or muscle groups can voluntarily exert in one maximal effort, when type of muscle contraction, movement velocity, and joint angle(s) are specified.

Strength Testing and Muscle Terminology

Physiologically, skeletal muscle strength is the ability of muscle fibers to generate maximal tension for a brief time interval. A muscle’s ability to generate maximal tension and to sustain tension for differing time intervals is dependent on the muscle’s cross-sectional area (the larger the cross-sectional area, the greater the strength), geometry (including the muscle fiber arrangement, length, moment arm, and angle of pennation), and physiology. Characteristics of muscle fibers have been classified based on twitch tension and fatigability. Different fiber types have different metabolic traits. Different types of muscle fibers are differentially stressed depending on the intensity and duration of the contraction. Ideally, strength tests should measure the ability of the muscle to develop tension rapidly and to sustain the tension for brief time intervals. In order to truly measure muscle tension, a measurement device must be directly attached to the muscle or tendon. Whereas this direct procedure has been performed [Komi, 1990], it is hardly useful as a routine clinical measure. Indirect measures are used to estimate the strength of muscle groups performing a given function, such as elbow flexion.

Muscles work together in groups and may be classified according to the major role of the group in producing movement. The prime mover, or agonist, is a muscle or muscle group that makes the major contribution to movement at a joint. The antagonist is a muscle or muscle group that has an opposite action to the prime mover(s). The antagonist relaxes as the agonist moves the body part through the ROM. Synergists are accessory muscles that contract and work with the agonist to produce the desired movement. Synergists may work by stabilizing proximal joints, preventing unwanted movement, and joining with the prime mover to produce a movement that one muscle group acting alone could not produce.

A number of terms and concepts are important toward understanding the nature and scope of strength capacity testing. Several of these terms are defined below; however, there are no universally accepted definitions for these terms.

Dynamic contraction—the output of muscles moving body segments [Kroemer, 1991].

Isometric—tension develops in a muscle, but the muscle length does not change and no movement occurs.

Static—same as isometric.

Isotonic—a muscle develops constant tension against a load or resistance. Kroemer [1991] suggests the term, isoforce, more aptly describes this condition.

Concentric—a contraction in which a muscle develops internal force that exceeds the external force of resistance, the muscle shortens, and movement is produced [O’Connel and Gowitzke, 1972].

Eccentric—a contraction in which a muscle lengthens while continuing to maintain tension [O’Connel & Gowitzke, 1972].

Isokinetic—a condition where the angular velocity is held constant. Kroemer [1991] prefers the term, isovelocity, to describe this type of muscle exertion.

Isoinertial—a static or dynamic muscle contraction where the external load is held constant [Kroemer, 1983].

Factors Influencing Muscle Strength and Strength Measurement

In addition to the anatomical and physiological factors affecting strength, other factors must be considered when strength testing. The ability of a muscle to develop tension depends on the type of muscle con­traction. Per unit of muscle, the greatest tension can be generated eccentrically, less can be developed isometrically, and the least can be generated concentrically. These differences in tension generating capacity are so great that the type of contraction being strength-tested requires specification.

Additionally, strength is partially determined by the ability of the nervous system to cause more motor units to fire synchronously. As one trains, practices an activity, or learns test expectations, strength can increase. Therefore, strength is affected by previous training and testing. This is an important consider­ation in standardizing testing and in retesting.

A muscle’s attachments define the angle of pull of the tendon on the bone and thereby the mechanical leverage at the joint center. Each muscle has a moment arm length, which is the length of a line normal to the muscle passing through the joint center. This moment arm length changes with the joint angle which changes the muscle’s tension output. Optimal tension is developed when a muscle is pulling at a 90D angle to the bony segment.

Changes in muscle length alter the force-generating capacity of muscle. This is called the length-tension relationship. Active tension decreases when a muscle is either lengthened or shortened relative to its resting length. However, after applying a precontraction stretch, or slightly lengthening a muscle and the series elastic component (connective tissue) prior to a contraction causes a greater amount of total tension to be developed [Soderberg, 1992]. Of course, excessive lengthening would reduce the tension-generating capacity.

A number of muscles cross over more than one joint. The length of these muscles may be inadequate to permit complete ROM of all joints involved. When a multijoint muscle simultaneously shortens at all joints it crosses, further effective tension development is prevented. This phenomena is called active insufficiency. For example, when the hamstrings are tested as knee flexors with the hip extended, less tension can be developed than when the hamstrings are tested with the hip flexed. Therefore, when testing the strength of multijoint muscles, the position of all involved joints must be considered.

The load-velocity relationship is also important in testing muscle strength. A load-velocity curve can be generated by plotting the velocity of motion of the muscle lever arm against the external load. With concentric muscle contractions, the least tension is developed at the highest velocity of movement and vice versa. When the external load equals the maximal force that the muscle can exert, the velocity of shortening reaches zero and the muscle contracts isometrically. When the load is increased further, the muscle lengthens eccentrically. During eccentric contractions, the highest tension can be achieved at the highest velocity of movement [Komi, 1973].

The force generated by a muscle is proportional to the contraction time. The longer the contraction time, the greater the force development up to the point of maximum tension. Slower contraction leads to greater force production because more time is allowed for the tension produced in contractile elements to be transferred through the noncontractile components to the tendon. This is the force-time relationship.

Tension in the tendon will reach the maximum tension developed by the contractile tissues only if the active contraction process is of adequate (even up to 300 msec) duration [Sukop and Nelson, 1974].

Subject effort or motivation, gender, age, fatigue, time of day, temperature, occupation, and dominance can also affect force or torque production capacity. Important additional considerations may be changes in muscle function as a result of pain, overstretching, immobilization, trauma, paralytic disorders, neurologic conditions, and muscle transfers.

Grading Systems and Parameters Measured

Clinically, the two most frequently used methods of strength testing are actually non-instrumented tests: the manual muscle test (MMT) and the functional muscle test (See Amundsen [1990] for more infor­mation on functional muscle tests). In each of these cases interval scaled grading criteria are operationally defined. However, a distinct advantage of using instruments to measure strength is that quantifiable units can be obtained, usually force or torque. Torque = force □ the distance between the point of force application and the axis of rotation:

T = Fx. (148.1)

An important issue in strength testing is deciding whether to measure force (a linear quantity) or torque (a rotational quantity). If the point of application of a force is closer to the axis of rotation, the muscle being assessed has a mechanical advantage as compared to when the point of contact is more distal. Therefore, when forces are measured, unless the measurement devices are applied at the same anatomical position for each test, force measurements can differ substantially even though actual muscle tension remains the same. If the strength testing device has an axis of rotation that can be aligned with the anatomical axis of rotation, then torque can be measured directly. When this is not the case, the moment arm can be measured and torque calculated. Force is more typically measured in whole-body exertions, such as lifting. Another issue is whether to measure and record peak or averaged values. However, if strength is defined as maximum torque production capacity, peak values are implied.

In addition to single numerical values, some strength measurement systems display and print force or torque (versus time) curves, angle-torque curves, and graphs. Computerized systems frequently com­pare the “involved” with the “uninvolved” extremity calculating “percent deficits.” As strength is consid­ered proportional to body weight (perhaps erroneously, see Delitto [1990]) force and torque measurements are frequently reported as a peak torque to body weight ratio. This is seemingly to facilitate use of normative data where present.

Methods and Instruments Used to Measure Muscle Strength

There are two broad categories of testing force or torque production capacity: one category consists of measuring the capacity of defined, local muscle groups (e. g., elbow flexors); the second category of tests consists of measuring several muscle groups on a whole-body basis performing a higher level task (e. g., lifting). The purpose of the test, required level of sensitivity, and expense are primary factors in selecting the method of strength testing. No single method has emerged as being clearly superior or more widely applicable. Like screwdrivers, different types and different sizes are needed depending upon job demands.

Many of the instrumented strength testing techniques, which are becoming more standardized clini­cally and which are almost exclusively used in engineering applications, are based on the concepts and methods of MMT. Although not used for performance capacity tests, because of the ease, practicality, and speed of manual testing, it is still considered a useful tool, especially diagnostically to localize lesions. Several MMT grading systems prevail. These differ in the actual test positions and premises upon which muscle grading is based. For example, the approach promoted by Kendall, McCreary, and Provance [1993] tests a specific muscle (e. g., brachioradialis) rather than a motion. The Daniels and Worthingham [Hislop and Montgomery, 1995] method tests motions (e. g., elbow flexion) which involve all the agonists and synergists used to perform the movement. The latter is considered more functional and less time consuming, but less specific. The reader is advised to consult these references directly for more informa­tion about MMT methods. Further discussion of non-instrumented tests is beyond the scope of this chapter.

An argument can be made for using isometric testing because the force or torque reflects actual muscle tension as the position of the body part is held constant and the muscle mechanics do not change. Additionally, good stabilization is easier to achieve, and muscle actions can be better isolated. However, some clinicians prefer dynamic tests, perceiving them as more reflective of function. An unfortunate fact is that neither static nor dynamic strength measurements alone can reveal whether strength is adequate for functional activities. However, strength measurements can be used with models and engineering analyses for such assessments.

Selected instrumented methods of measuring force or torque production capacity are listed and compared in Table 148.3. Table 148.3 is by no means comprehensive. More indepth review and compar­isons of various methods can be found in Amundsen [1990] and Mayhew and Rothstein [1985]. Figure 148.3 Illustrates three common instruments used to measure strength.

Key Concepts in Measuring Strength

Because of the number of factors influencing strength and strength testing (discussed in a previous section), one can become discouraged rather than challenged when faced with the need to measure strength. Optimally, strength testing would be based on the “worst case” functional performance demands required by an individual in his or her daily life. “Worst case” testing requires knowing the performance demands of tasks including the positions required, types of muscle contractions, and so on.

In the absence of such data, current strategy is to choose the instruments and techniques that maximally stress the system under a set of representative conditions that either (a) seem logical based on knowledge of the task, or (b) have been reported as appropriate and reliable for the population of interest. An attempt is made to standardize the testing in terms of contraction-type, test administration instructions, feedback, warm-up, number of trials, time of day, examiner, duration of contraction (usually 4 to 6 seconds), method and location of application of force, testing order, environmental distractions, subject posture and position of testing, degree of stabilization, and rest intervals between exertions (usually 30 seconds to 2 minutes) [Chaffin, 1975; Smidt and Rogers, 1982]. In addition, the subject must be observed for muscle group substitutions and “trick” movements.

Speed of Movement

Speed of movement refers to the rate of movement of the body or body segments. The maximum movement speed that can be achieved represents another unique performance capacity of an identified system that is responsible for producing motion. Everyday living, work, and sport tasks are commonly described in terms of the speed requirements (e. g., repetitions per minute or per hour). For physical tasks, such descriptions translate to translational motion speeds (e. g., as in lifting) as well as rotational motion speeds (i. e., movement about a dof of the joint systems involved). Thus, there is important motivation to characterize this capacity.

Speed of Movement Terminology

Speed of movement must be differentiated from speed of contraction. Speed of contraction refers to how fast a muscle generates tension. Two body parts may be moving through an arc with the same speed of movement; however, if one part has a greater mass, its muscles must develop more tension per unit of time to move the heavier body part at the same speed as the lighter body part.

Speed, velocity, and acceleration also can be distinguished. The terms velocity and speed are often used interchangeably; however, the two quantities are frequently not identical. Velocity means the rate of motion in a particular direction. Acceleration results from a change in velocity over time. General


Repetition maximum

Amount of weight a subject can lift a given number of times and no more; one determines either a one repetition maximum (1-RM) or a ten repetition maximum (10-RM). A 1-RM is the maximum amount of weight a subject can lift once; a 10-RM is the amount of weight a subject can lift 10 times; a particular protocol to determine RMs is defined [DeLorme and Watkins, 1948]; measures dynamic strength in terms of weight (pounds or kilograms) lifted.

Hand-held dynamometer

Device held in the examiner’s hand used to test strength; devices use either hydraulics, strain gauges (load-cells), or spring systems (See Fig. 148.3a); used with a “break test” (the examiner exerts a force against the body segment to be tested until the part gives way) or a “make test” (the examiner applies a constant force while the subject exerts a maximum force against it); “make tests” are frequently preferred for use with hand-held dynamometers [Bohannon, 1990; Smidt, 1984]; measures force; unclear whether test measures isometric or eccentric force (this may depend on whether a “make test” or a “break test” is used).

Cable tensiometer

One end of a cable is attached to an immovable object and the other end is attached to a limb segment; the tensiometer is placed between the sites of fixation; as the cable is pulled, it presses on the tensiometer’s riser which is connected to a gauge (See discussion in Mayhew and Rothstein [1985]); measures isometric force.

Strain gauge

Electroconductive material applied to metal rings or rods; a load applied to the ring or bar deforms the metal and a gauge; deformation of the gauge changes the electrical resistance of the gauge causing a voltage variation; this change can be converted and displayed using a strip chart recorder or digital display; measures isometric force.

Isokinetic dynamometer

Constant velocity loading device; several models marketed by a number of different companies; most consist of a movable lever arm controlled by an electronic servomotor that can be preset for selected angular velocities usually between 0D and 500D per second; when the subject attempts to


Requires minimal equipment (weights); inexpensive and easy to administer; frequently used informally to assess progress in strength training.

Similar to manual muscle testing (MMT) in test positions and sites for load application; increased objectivity over MMT; portable; easy to administer; relatively inexpensive; commercially available from several suppliers; adaptable for a variety of test sites; provide immediate output; spring and hydraulic systems are non­electrical; load-cell based systems provide more precise digital measurements.

Mostly used in research settings; evidence presented on reliability when used with healthy subjects [Clarke, 1954; Clarke, Bailey and Shay, 1952]; relatively inexpensive.

Mostly used in research settings; increased sensitivity for testing strong and weak muscles.

Permits dynamic testing of most major body segments; especially useful for stronger movements; most devices provide good stabilization; measures reciprocal muscle contractions; widespread


Uses serial testing of adding weights which may invalidate subsequent testing; no control for speed of contraction or positioning; minimal information available on the reliability and validity of this method.

Stabilization of the device and body segment can be difficult; results can be affected by the examiner’s strength; limited usefulness with large muscle groups; spring-based systems fatigue over time becoming inaccurate; range and sensitivity of the systems vary; shape of the unit grasped by the examiner and shape of the end-piece vary in comfort, and therefore the force a subject or examiner is willing to exert; more valuable for testing subjects with weakness than for less involved or healthy subjects due to range limits within the device (See discussion in Bohannon [1990]).

Requires special equipment for testing; testing is time-consuming and some tests require two examiners; unfamiliar to most clinicians; not readily available; less sensitive at low force levels.

Strain gauges require frequent calibration and are sensitive to temperature variations; to be accurate the body part must pull or push against the gauge in the same line that the calibration weights were applied; unfamiliar to most clinicians; not commercially available; difficult to interface the device comfortably with the subject.

Devices are large and expensive; need calibration with external weights or are “self-calibrating;” signal damping and “windowing” may affect data obtained; angle-specific measurements may not be accurate if a damp is used because torque readings do not relate to the

Accelerate beyond the pre-set machine speed, the machine resists the movement; a load cell measures the torque needed to prevent body part acceleration beyond the selected speed; computers provide digital displays and printouts (See typical device in Fig. 148.3b); measures isokinetic-concentric (and in some cases, isokinetic-eccentric) and isometric strength; provides torque (or occasionally force) data; debate exists about whether data are ratio-scaled or not; accounting for the weight of the segment permits ratio-scaling [Winter, Wells, and Orr, 1981].

Hand dynamometer

Instruments to measure gripping or pinching strength specifically for the hand; usually use a spring scale or strain-gauge system (See typical grip strength testing device in Fig. 148.3c); measures isometric force.

Clinical acceptance; also records angular data, work, power, and endurance-related measures; provides a number of different reporting options; also used as exercise devices.

Readily available from several suppliers; easy to use; relatively inexpensive; widespread use; some normative data available.

Goniometric measurements; joints must be aligned with the mechanical axis of the machine; inferences about muscle function in daily activities from isokinetic test results have not been validated; data obtained between different brands are not interchangeable; adequate stabilization may be difficult to achieve for some movements; may not be usable with especially tall or short persons.

Only useful for the hand; different brands not interchangeable; normative data only useful when reported for the same instrument and when measurements are taken with the same body position and instrument setting; must be recalibrated frequently.

Velocity and acceleration measurements are beyond the intent of this chapter. Reaction speed and response speed are other related variables also not considered.

Factors Influencing Speed of Movement and Speed of Movement Measurements

Muscles with larger moment arms, longer muscle fibers, and less pennation tend to be capable of generating greater speed. Many of the same factors influencing strength, discussed previously, such as muscle length, fatigue, and temperature affect the muscle’s contractile rate. The load-velocity relationship is especially important when testing speed of movement. In addition to these and other physiological factors, speed can be reduced by factors such as friction, air resistance, gravity, unnecessary movements, and inertia [Jensen and Fisher, 1979].

Parameters Measured

Speed of movement can be measured as a linear quantity or as an angular quantity. Typically, if the whole body is moving linearly in space as in walking or running, a point such as the center of gravity is picked, and translational motion is measured. Also, when an identified point on a body segment (e. g., the tip of the index finger) is moved in space, translational movement is observed, and motion is measured in translational terms. If the speed of a rotational motion system (e. g., elbow flexors) is being measured, then the angular quantity is determined. As the focus here is on measuring isolated neuromuscular performance capacities, the angular metric is emphasized. Angular speed of a body segment is obtained by: angular speed = change in angular position/change in time:

□ □ —. (148.2)

□ t

Thus, speed of movement may be expressed in revolutions, degrees, or radians per unit of time, such as degrees per second (deg/s).

Measurement of Neuromuscular Performance Capacities


FIGURE 148.3 Three types of instrumented strength testing devices are shown: (a) a representative example of a typical hand-held dynamometer; (b) an example of an isokinetic strength testing device; (c) a hand dynamom­eter used to measure grip strength

Another type of speed measure applies to well-defined (over fixed angle or distance) cyclic motions. Here repetitions per unit time or cycles per unit time measures are sometimes used. However, in almost every one of these situations, speed can be expressed in degrees per second or meters per second. The latter units are preferred because they allow easier comparison of speeds across a variety of tasks. The only occasion when this is difficult is when translation motion is not in a simple straight line, such as when a person is performing a complex assembly task with multiple subtasks.

The issue of whether to express speed as maximum, averaged, or instantaneous values must also be decided based on which measure is a more useful indicator of the performance being measured. In addition to numerical reporting of speed data, time-history graphs of speed may be helpful in comparing some types of performance.

Instruments Used to Measure Speed of Movement

When movement time is greater than a few seconds and the distance is known, speed can be measured with a stop watch or with switch plates, such as the time elapsed in moving between two points or over a specified angle. With rapid angular joint movements, switch plates or electrogoniometers with electronic timing devices are required. Speeds can also be computed from the distance or angle and time data available from cinematography, optoelectric movement monitoring systems, and videotape systems. Some dynamic strength testing devices involve presetting a load and measuring the speed of movement.

In addition, accelerometers can be used to measure acceleration directly, and speed can be derived through integration. However, piezoelectric models have no steady-state response and may not be useful for slower movements. Single accelerometers are used to measure linear motion. Simple rotatory motions require two accelerometers. Triaxial accelerometers are commercially available that contain three pre­mounted accelerometers perpendicular to each other. Multiple accelerometer outputs require appropriate processing to resolve the vector component corresponding to the desired speed. Accelerometers are most appropriately used to measure acceleration when they are mounted on rigid materials. Accelerometers have the advantage of continuously and directly measuring acceleration in an immediately usable form. They can also be very accurate if well-mounted. Because they require soft tissue fixation and cabling or telemetry, they may alter performance and further error may be induced by relative motion of the device and tissues. The systems are moderately expensive (See discussion of accelerometers in Robertson and Sprigings [1987]).

Key Concepts in Speed of Movement Measurement

As discussed, maximum speed is determined when there is little stress on torque production resources. As resistance increases, speed will decrease. Therefore, the load must be considered and specified when testing speed. Because speed of movement data are calculated from displacement and temporal data, a key issue is minimizing error which might result from collecting this information. Error can result from inaccurate identification of anatomical landmarks, improper calibration, perspective error, instrument synchronization error, resolution, digitization error, or vibration. The sampling rate of some of the measurement systems may become an issue when faster movements are being analyzed. In addition, the dynamic characteristics of signal conditioning systems should be reported.


Endurance is the ability of a system to sustain an activity for a prolonged time (static endurance) or to perform repeatedly (dynamic endurance). Endurance can apply to the body as a whole, a particular body system, or to specific neuromuscular functional units. High levels of endurance imply that a given level of performance can be continued for a long time period.

Endurance Terminology

General endurance of the body as a whole is traditionally considered cardiovascular endurance or aerobic capacity. Cardiovascular endurance is most frequently viewed in terms of V02max. This chapter considers only endurance of neuromuscular systems. Although many central and peripheral anatomic sites and physiologic processes contribute to a loss of endurance, endurance of neuromuscular functional units is also referred to as muscular endurance.

Absolute muscle endurance is defined as the amount of time that a neuromuscular system can continue to accomplish a specified task against a constant resistance (load and rate) without relating the resistance to the muscle’s strength. Absolute muscle endurance and strength are highly correlated. Conversely, strength and relative muscle endurance are inversely related. That is, when resistance is adjusted to the person’s strength, a weaker person tends to demonstrate more endurance than a stronger person. Fur­thermore, the same relationships between absolute and relative endurance and strength are correlated by type of contraction; in other words, there is a strong positive correlation between isotonic strength and absolute isotonic endurance and vice versa for strength and relative isotonic endurance. The same types of relationships exist for isometric strength and isometric endurance [Jensen and Fisher, 1979].

Factors Influencing Neuromuscular Endurance and Measurement of Endurance

Specific muscle fiber types, namely fast-twitch fatigue-resistant fibers (FR), generate intermediate levels of tension and are resistant to short-term fatigue (a duration of about 2 minutes or intermittent stimu­lation). Slow-twitch fibers (S) generate low levels of tension slowly, and are highly resistant to fatigue. Muscle contractions longer than 10 seconds, but less than 2 minutes, will reflect local muscle endurance [Astrand and Rodahl, 1986]. For durations longer than 2 minutes, the S fibers will be most stressed. A submaximal isometric contraction to the point of voluntary fatigue will primarily stress the FR and S fibers [Thorstensson and Karlsson, 1976]. Repetitive, submaximal, dynamic contractions continued for about 2 to 6 minutes will measure the capacity of FR and S fibers. Strength testing requires short duration and maximal contractions; therefore, to differentiate strength and endurance testing, the duration and intensity of the contractions must be considered.

Because strength affects endurance, all of the factors discussed previously as influencing strength, also influence endurance. In addition to muscle physiology and muscle strength, endurance is dependent upon the extensiveness of the muscle’s capillary beds, the involved neuromuscular mechanisms, contrac­tion force, load, and the rate at which the activity is performed.

Endurance time, or the time for muscles to reach fatigue, is a function of the contraction force or load [von Rohmert, 1960]. As the load (or torque required) increases, endurance time decreases. Also, as speed increases, particularly with activities involving concentric muscle contractions, endurance decreases.

Parameters Measured

Endurance is how long an activity can be performed at the required load and rate level. Thus, the basic unit of measure is time. Time is the only measure of how long it takes to complete a task. If we focus on a given variable (e. g., strength, speed, or endurance), it is necessary to either control or measure the others. When the focus is endurance, the other factors of force or torque, speed, and joint angle, can be described as conditions under which endurance is measured. Because of the interactions of endurance and load, or endurance and time, for examples, a number of endurance-related measures have evolved. These endurance-related measures have clouded endurance testing.

One endurance-related measure uses either the number of repetitions that can be performed at 20, 25, or 50 percent of maximum peak torque or force. The units used to reflect endurance in this case are number of repetitions at a specified torque or force level. One difficulty with this definition has been described previously, that is, the issue of relative versus absolute muscle endurance. Rothstein and Rose [1982] demonstrated that elderly subjects with selected muscle fiber type atrophy were able to maintain 50 percent of their peak torque longer than young subjects. However, if a high force level is required to perform the task, then the younger subject would have more endurance in that particular activity [Rothstein, 1982]. Another difficulty is that the “repetition method” can be used only for dynamic activities. If isometric activities are involved, then the time an activity can be sustained at a specified force or torque level is measured. Why have different units of endurance? Time could be used in both cases. Furthermore, the issue of absolute versus relative muscle endurance becomes irrelevant if the demands of the task are measured.

Yet another method used to reflect endurance is to calculate an endurance-related work ratio. Many isokinetic testing devices, such as the one shown in Fig. 148.3b, Will calculate work (integrate force or torque over displacement). In this case, the total amount of work performed in the first five repetitions is compared with the total amount of work performed in the last five repetitions of a series of repetitions (usually 25 or more). Work degradation reflects endurance and is reported as a percentage. An additional limitation of using these endurance ratios is that work cannot be determined in isometric test protocols. Mechanically there is no movement, and no work is being performed.

Overall, the greatest limitation with most endurance-related approaches is that the measures obtained cannot be used to perform task-related assessments. In a workplace assessment, for example, one can determine how long a specific task (defined by the conditions of load, range, and speed) needs to be performed. Endurance-related metrics can be used to reflect changes over time in a subject’s available endurance capacity; however, endurance-related metrics cannot be compared to the demands of the task. Task demands are measured in time or repetitions (e. g., 10) with a given rate (e. g., 1/0.5 h) from which total time (e. g., 5 h) can be calculated. A true endurance measure (versus an endurance-related measure) can serve both purposes. Time reflects changes in endurance as the result of disease, disuse, training, or rehabilitation and also can be linked to task demands.

Methods and Instruments Used to Measure Neuromuscular Endurance

Selection of the method or instrument used to measure endurance depends on the purpose of the mea­surement and whether endurance or endurance-related measures will be obtained. As in strength testing, endurance tests can involve simple, low level tasks or whole-body, higher level activities. The simplest method of measuring endurance is to define a task in terms of performance criteria and then time the performance with a stop watch. A subject is given a load and a posture and asked to hold it “as long as possible” or to move from one point to another point at a specific rate of movement for “as long as possible.”

An example of a static endurance test is the Sorensen test used to measure endurance of the trunk extensors [Biering-Sorensen, 1984]. This test measures how long a person can sustain his or her torso in a suspended prone posture. The individual is not asked to perform a maximal voluntary contraction, but an indirect calculation of load is possible [Smidt and Blanpied, 1987].

An example of a dynamic endurance test is either a standardized or non-standardized, dynamic isoin­ertial (see previous description in the section on strength testing) repetition test. In other words, the subject is asked to lift a known load with a specified body part or parts until defined conditions can no longer be met. Conditions such as acceleration, distance, method of performance, or speed may or may not be controlled. The more standardized of these tests, particularly those which involve lifting capacity, are reported and projections about performance capacity over time are estimated [Snook, 1978]. Ergometers and some of the isokinetic dynamometers discussed previously measure work, and several can calculate endurance-related ratios. These devices could be adapted to measure endurance in time units.

Key Concepts in Measuring Muscle Endurance

Of the four variables of human performance discussed in this chapter, endurance testing is the least developed and standardized. Except for test duration and rest intervals, attention to the same guidelines as described for strength testing is currently recommended.

Reliability, Validity, and Limitations in Testing

Space does not permit a complete review of these important topics. However, a few key comments are in order. First, it is important to note that reliability and validity are not inherent qualities of instruments, but exist in the measurements obtained only within the context in which they are tested. Second, reliability and validity are not either present or absent but are present or absent along a continuum. Third, traditional quantitative measures of reliability might indicate how much reliability a given measurement method demonstrates, but not how much reliability is actually needed. Fourth, technology has advanced to the extent that it is generally possible to measure physical variables such as time, force, torque, angles, and speed accurately, repeatably, and with high resolution. Lastly, clinical generalizability of human performance capacity measures ultimately results from looking at the body of literature on reliability as a whole and not from single studies.

For these types of variables, results of reliability studies basically report that: (1) if the instrumentation is good, and (2) if established, optimal procedures are carefully followed, then results of repeat testing will usually be in the range of about 5 to 20% of each other. This range of repeatability depends on: (1) the particular variable being measured (i. e., repeated endurance measures will differ more than repeated measures of hinge joint EOM), and (2) the magnitude of the given performance capacity (i. e., errors are often in fixed amounts such as 3D for ROM; thus, 3D out of 180D is smaller percentage-wise than 3D out of 20°). One can usually determine an applicable working value (e. g., 5 or 20%) by careful review of the relevant reliability studies. Much of the difference obtained in test-retest results is because of limitations in how well one can reasonably control procedures and the actual variability of the parameter being measured, even in the most ideal test subjects. Measurements should be used with these thoughts in mind. If a specific application requires extreme repeatability, then a reliability study should be conducted under conditions that most closely match those in which the need arises. Reliability discussions specific to some of the focal measures of this chapter are presented in Amundsen [1990], Hellebrandt, Duvall, and Moore [1949], Mayhew and Rothstein [1985], and Miller [1985].

Measurements can be reliable but useless without validity. Most validity studies have compared the results of one instrument to another instrument or to known quantities. This is the classical type of validity testing which is an effort to determine whether the measurement reflects the variable being measured. In the absence of a “gold standard” this type of testing is of limited value. In addition to traditional studies of the validity of measurements, the issue of the validity of the inferences based on the measurements is becoming increasingly important [Rothstein and Echternach, 1993]. That is, can the measurements be used to make inferences about human performance in real life situations? Unfortunately, measurements which have not demonstrated more than content validity are frequently used as though they are predictive. The validity of the inferences made from human performance data needs to be rigorously addressed.

Specific measurement limitations were briefly addressed in Tables 148.2 and 148.3 and in the written descriptions of various measurement techniques and instruments. Other limitations have more to do with interpreting the data. A general limitation is that performance variables are not fixed human attributes. Another limitation is that population data are limited and available normative data are, unfortunately, frequently extrapolated to women, older persons, and so on [Chaffin and Andersson, 1991]. Some nor­mative data suggest the amount of resources such as strength, ROM, speed of movement, and endurance required for given activities; other data suggest the amount available. As previously mentioned, these are two different issues. Performance measurements may yield information about the current status of per­formance, but testing rarely indicates the cause or the nature of dysfunction. More definitive, diagnostic studies are used to answer these questions. Whereas, considerable information exists with regard to measuring performance capacities of human systems, much less energy has been directed to understanding requirements of tasks. The link between functional performance in tasks and laboratory-acquired mea­surements is a critical question and a major limitation in interpreting test data. The ERM addresses several of these limitations by using a multidimensional, individualized, cause-and-effect model.

Performance Capacity Space Representations

In both the study and practice, performance of neuromuscular systems has been characterized along one or two dimensions of performance at a time. However, human subsystems function within a multi­dimensional performance space. ROM/EOM, strength, movement speed, and endurance capacities are
Not only interdependent, but may also vary uniquely within individuals. Multiple measurements are necessary to characterize a person’s performance capacity space, and performance capacity is dependent on the task to be performed. Therefore, both the individual and the task must be considered when selecting measurement tools and procedures [Chaffin and Andersson, 1991].

Measurement of Neuromuscular Performance CapacitiesIn many of the disciplines in which human per­formance is of interest, traditional thinking has often focused on single number measures of ROM, strength, speed, etc. More recent systems engineer­ing approaches [Kondraske, 1999] emphasize con­sideration of the performance envelope of a given system and suggest ways to integrate single mea­surement points that define the limits of perfor­mance of a given system [Vasta and Kondraske,

1997]. Figure 148.4 il Lustrates a three-dimensional performance envelope derived from torque, angle, and velocity data for the knee extensor system. The additional dimension of endurance can be repre­sented by displaying this envelope after performing an activity for different lengths of time. A higher level, composite performance capacity, as is some – FIGURE 148.4 An exampk of a torque-angle-velocity

Times needed, could be derived by computing the performance envelope for the knee extensor system.

I j u i o i – Source: Vasta PJ, Kondraske GV. 1994. A multi-dimen –

Volume enclosed by this envelope. Such represen –

Sional performance space model for the human knee

Tations also facilitate assessment of the given system

Extensor (technical Report 94-001R). p 11. University of

In a specific task; that is, a task is defined as a point

Texas at Arlington, Human Performance Institute, in this space that will either fall inside or outside Arlington, Texas. With permission. the envelope.


In conclusion, human movement is so essential that it demands interest and awe from the most casual observer to the most sophisticated scientists. The complexity of performance is truly inspiring. We are challenged to understand it! We want to reduce it to comprehensible units and then enhance it, reproduce it, restore it, and predict it. To do so, we must be able to define and quantify the variables. Hence, an array of instruments and methods have emerged to measure various aspects of human performance. To date, measurement of neuromuscular performance capacities along the dimensions of ROM/EOM, strength, speed of movement, and endurance represents a giant stride but only the “tip of the iceberg.” Progress in developing reliable, accurate, and valid instruments and in understanding the factors influ­encing the measurements cannot be permitted to discourage us from the larger issues of applying the measurements toward a purpose. Yet, single measurements will not suffice; multiple measurements of different aspects of performance will be necessary to fully characterize human movement.

Defining Terms

Endurance: The amount of time a body or body segments can sustain a specified static or repetitive activity.

Extremes of motion (EOM): The end ranges of motion at a joint measured in degrees.

Muscle strength: The maximal amount of torque or force production capacity that a muscle or muscle


Neuromuscular functional units: Systems (that is, the combination of nerves, muscles, tendons, liga­ments, and so on) responsible for producing basic movements.

Range of motion (ROM): The amount of movement that occurs at a joint, typically measured in

Degrees. ROM is usually measured by noting the extremes of motion, or as the difference between the extreme motion and the reference position.

Speed of movement: The rate of movement of the body or body segments.


Amundsen LR. 1990. Muscle Strength Testing: Instrumented and Non-Instrumented Systems. New York, Churchill Livingstone.

Вstrand P-O., Rodahl K. 1986. Textbook of Work Physiology: Physiological Bases of Exercise, 3rd ed. New York, McGraw-Hill.

Biering-Sorensen F. 1984. Physical measurements as risk indicators for low back trouble over a one year period. Spine 9:106-119.

Bohannon RW. 1990. Muscle strength testing with hand-held dynamometers. In LR Amundsen (ed), Muscle Strength Testing: Instrumented and Non-Instrumented Systems. pp 69-88. New York, Churchill Livingstone.

Chaffin DB. 1975. Ergonomics guide for the assessment of human strength. Amer. Ind. Hyg. J. 36:505-510.

Chaffin DB, Andersson GB. 1991. Occupational Biomechanics, 2nd ed. New York, John Wiley & Sons, Inc.

Clarke HH. 1954. Comparison of instruments for recording muscle strength. Res. Q. 25:398-411.

Clarke HH, Bailey TL, Shay CT. 1952. New objective strength tests of muscle groups by cable-tension methods. Res. Q. 23:136-148.

Clarkson HM, Gilewich GB. 1989. Musculoskeletal Assessment: Joint Range of Motion and Manual Muscle Strength. Baltimore, Williams & Wilkins.

Delitto, A. 1990. Trunk strength testing. In LR Amundsen (ed), Muscle Strength Testing: Instrumented and Non-Instrumented Systems, pp 151-162. New York, Churchill Livingstone.

DeLorme TL, Watkins AL. 1948. Technics of progressive resistive exercise. Arch. Phys. Med. Rehabil. 29:263-273.

Hellebrandt FA, Duvall EN, Moore ML. 1949. The measurement of joint motion: Part III—Reliability of goniometry. Phys. Ther. Rev. 29:302-307.

Hislop HJ, Montgomery J. 1995. Daniel’s and Worthingham’s Muscle Testing: Techniques of Manual Exam­ination, 6th ed. Philadelphia, WB Saunders Company.

Jensen CR, Fisher AG. 1979. Scientific Basis of Athletic Conditioning, 2nd ed. Philadelphia, Lea & Febiger.

Kendall FP, McCreary EK, Provance PG. 1993. Muscles: Testing and Function, 4th ed. Baltimore, Williams & Wilkins.

Komi PV. 1973. Measurement of the force-velocity relationship in human muscle under concentric and eccentric contractions. In S Cerquiglini, A Venerando, J Wartenweiler (eds), Biomechanics III, pp 224-229. Baltimore, University Park Press.

Komi PV. 1990. Relevance of in vivo force measurements to human biomechanics. J. Biomech. 23 (suppl. 1): 23-34.

Kondraske GV. 1999. A working model for human system-task interfaces. In JD Bronzino (ed) Biomedical Engineering Handbook, 2nd ed. Boca Raton Fla, CRC Press, Inc.

Kroemer KHE. 1983. An isoinertial technique to assess individual lifting capability. Human Factors 25:493-506.

Kroemer KHE. 1991. A taxonomy of dynamic muscle exertions. J. Hum. Muscle Perform. 1:1-4.

Mayhew TP, Rothstein JM 1985. Measurement of muscle performance with instruments. In JM Rothstein (ed), Measurement in Physical Therapy, pp 57-102. New York, Churchill Livingstone.

Miller PJ. 1985. Assessment of joint motion. In JM Rothstein (ed), Measurement in Physical Therapy, pp 103-136. New York, Churchill Livingstone.

Norkin CC, White DJ. 1995. Measurement of Joint Motion: A Guide to Goniometry, 2nd ed. Philadelphia, FA Davis Company.

O’Connel AL, Gowitzke B. 1972. Understanding the Scientific Bases of Human Movement. Baltimore, Williams & Wilkins.

Palmer ML, Epler M. 1998. Fundamentals of Musculoskeletal Assessment Techniques, 2nd ed. Philadelphia, JB Lippincott Company.

Panjabi MM, White III AA, Brand RA. 1974. A note on defining body parts configurations. J. Biomech. 7:385-387.

Robertson G, Sprigings E. 1987. Kinematics. In DA Dainty, RW Norman (eds), Standardizing Biome­chanical Testing in Sport, pp 9-20. Champaign, Ill, Human Kinetics Publishers, Inc.

Robson P. 1966. A method to reduce the variable error in joint range measurement. Ann. Phys. Med. 8:262-265.

Rothstein JM. 1982. Muscle biology: Clinical considerations. Phys. Ther. 62:1823-1830.

Rothstein JM, Echternach JL. 1993. Primer on Measurement: An Introductory Guide to Measurement Issues. Alexandria, Va, American Physical Therapy Association.

Rothstein JM, Rose SJ. 1982. Muscle mutability—Part II: Adaptation to drugs, metabolic factors, and aging. Phys. Ther. 62:1788-1798.

Soderberg GL. 1992. Skeletal Muscle Function. In DP Currier, RM Nelson (eds), Dynamics of Human Biologic Tissues, pp 74-96. Philadelphia, FA Davis Company.

Smidt GL. 1984. Muscle Strength Testing: A System Based on Mechanics. Iowa City, IA, SPARK Instruments and Academics, Inc.

Smidt GL, Blanpied PR. 1987. Analysis of strength tests and resistive exercises commonly used for low – back disorders. Spine 12:1025-1034.

Smidt GL, Rogers MR. 1982. Factors contributing to the regulation and clinical assessment of muscular strength. Phys. Ther. 62:1284-1290.

Snook SH. 1978. The design of manual handling tasks. Ergonomics 21:963-985.

Sukop J, Nelson RC. 1974. Effects of isometric training in the force-time characteristics of muscle contractions. In RC Nelson, CA Morehouse (eds), Biomechanics IV, pp 440-447. Baltimore, Uni­versity Park Press.

Thorstensson A, Karlsson J. 1976. Fatiguability and fibre composition of human skeletal muscle. Acta Physiol. Scand. 98:318-322.

Vasta PJ, Kondraske GV. 1994. A multi-dimensional performance space model for the human knee extensor (Technical Report 94-001R). University of Texas at Arlington, Human Performance Insti­tute, Arlington, TX.

Vasta PJ, Kondraske GV. 1997. An approach to estimating performance capacity envelopes: Knee extensor system example. Proc. 19th Ann. Eng. Med. Biol. Soc. Conf, pp 1713-1716.

Von Rohmert W. 1960. Ermittlung von erholungspausen fur statische arbeit des menschen, Int. Z. Angew. Physiol. 18:123-124.

West CC. 1945. Measurement of joint motion. Arch. Phys. Med. 26:414-425.

White III AA, Panjabi MM. 1990. Clinical Biomechanics of the Spine, 2nd ed. Philadelphia, JB Lippincott Company.

Wiechec FJ, Krusen FH. 1939. A new method of joint measurement and a review of the literature. Am. J. Surg. 43:659-668.

Winter DA, Wells RP, Orr GW. 1981. Errors in the use of isokinetic dynamometers. Eur. J. Appl. Physiol. 46:397-408.

Further Information

Human Performance Measurement, Inc. 1998. APM I Portable Electronic Goniometer: User’s Manual. PO Box 1996, Arlington, TX 76004-1996.

Journals: Clinical Biomechanics, Journal of Biomechanics, Medicine and Science in Sports and Exercise, Physical Therapy.

Smith SS, Kondraske GV. 1987. Computerized system for quantitative measurement of sensorimotor aspects of human performance. Phys. Ther. 67:1860-1866.

Task Force on Standards for Measurement in Physical Therapy. 1991. Standards for tests and measure­ments in physical therapy practice. Phys. Ther. 71:589-622.

Jones, R. D. “Measurement of Sensory-Motor Control Performance Capacities: Tracking Tasks.” The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000

A Working Model for Human System-Task Interfaces


Basic Principles

General Systems Performance Theory • Monadology • The Elemental Resource Model

Application Issues

Conceptual, Low-Tech Practical Application • Conceptual, Theoretical Application • Application “in Part” • Rigorous,

George V. Kondraske High-Tech Application

University of Texas at Arlington 147.4 Conclusion

Humans are complex systems. Our natural interest in ourselves and things that we do has given rise to the study of this complex system at every conceivable level ranging from genetic, through cellular and organ systems, to interactions of the total human with the environment in the conduct of purposeful activities. At each level, there are corresponding practitioners who attempt to discover and rectify or prevent problems at the respective level. Some practitioners are concerned with specific individuals, while others (e. g., biomedical scientists and product designers) address populations as a whole. Problems dealt with span medical and nonmedical contexts, often with interaction between the two. Models play a key role not only in providing conceptual understanding of key issues at each level but also in describing relationships between various levels and providing frameworks that allow practitioners to obtain reason­ably predictable results in a systematic and efficient fashion. In this chapter, a working model for human system-task interfaces is presented. Any such model must, of course, not only consider the interface per se but also representations of the human system and tasks. The model presented here, the elemental resource model (ERM), represents the most recent effort in a relatively small family of models that attempt to address similar needs.


The interface between a human and a task of daily living (e. g., work, recreation, or other) represents a level that is quite high in the hierarchy noted above. One way in which to summarize previous efforts directed at this level, across various application contexts, is to recognize two different lines along which study has evolved: (1) bottom-up and (2) top-down. Taken together, these relative terms imply a focus of interest at a particular level of convergence. Here, this special level is termed the human-task interface level. It is emphasized that bottom-up and top-down terms are used here to characterize the general course of development and not specific approaches applied at a particular instant in time. A broad view is necessary to grapple with the many previous efforts that either are, or could be, construed to be pertinent.

The biomedical community has approached the human-task interface largely along the bottom-up path. This is not surprising given the historical evolution of interest first in anatomy (human structure) and then physiology (function). The introduction of chemistry (giving rise to biochemistry) and the refinement of the microscope provided motivations to include even lower hierarchical levels of inquiry and of a substantially different character. Models in this broad bottom-up category begin with anatomic components and include muscles, nerves, tendons (or subcomponents thereof), or subsets of organs (e. g., heart, lungs, vasculature, etc.). They often focus on relationships between components and exhibit a scope that stays within the confines of the human system. Many cause-and-effect models have been developed at these lower levels for specific purposes (e. g., to understand lines of action of muscle forces and their changes during motion about a given joint).

As a natural consequence of linkages that occur between hierarchical levels and our tendency to utilize that which exists, consideration of an issue at any selected level (in this case, the human-task interface level) brings into consideration all lower levels and all models that have been put forth with the stated purpose of understanding problems or behaviors at the original level of focus. The amount of detail that is appropriate or required at these original, lower levels results in great complexity when applied to the total human at the human-task interface level. In addition, many lower-level modeling efforts (even those which are quantitative) are aimed primarily at obtaining a basic scientific understanding of human physiology or specific pathologies (i. e., pertaining to populations of humans). In such circumstances, highly specialized, invasive, and cumbersome laboratory procedures for obtaining the necessary data to populate models are justified. However, it is difficult and sometimes impossible to obtain data describing a specific individual to utilize in analyses when such models are extended to the human-task interface level. Another result of drawing lower-level models (and their approaches) into the human-task interface context is that the results have a specific and singular character (e. g., biomechanical versus neuromuscular control versus psychologic, etc.) [e. g., Card et al., 1986; Delp et al., 1990; Gottlieb et al., 1989; Hemami, 1988; Schoner & Kelso, 1988]. Models that incorporate most or all of the multiple aspects of the human system or frameworks for integrating multiple lower-level modeling approaches have been lacking. Lower – level models that serve meaningful purposes at the original level of focus have provided and will continue to provide insights into specific issues related to human performance at multiple levels of considerations. However, their direct extension to serve general needs at the human-task interface level has inherent problems; a different approach is suggested.

A top-down progression can be observed over the major history in human factors/ergonomic [Gilbreth & Gilbreth, 1917; Taylor, 1911] and vocational assessment [e. g., Botterbusch, 1987] fields (although the former has more recently emphasized a “human-centered” concept with regard to design applications). In contrast to the bottom-up path, in which anatomic components form the initial basis of modeling efforts, the focus along the top-down path begins with consideration of the task or job that is to be performed by the total human. The great variety in the full breadth of activities in which humans can be engaged gives rise to one aspect of complexity at this level that pertains to taxonomies for job and task classification [e. g., Fleishman & Quaintance, 1984; Meister, 1989; U. S. Department of Labor, 1992]. Another enigmatic aspect that quickly adds complexity with respect to modeling concerns the appropriate level to be used to dissect the items at the highest level (e. g., jobs) into lower-level components (e. g., tasks and subtasks). In fact, the choice of level is complicated by the fact that no clear definition has evolved for a set of levels from which to choose.

After progressing down through various levels at which all model elements represent tasks and are completely outside the confines of the human body, a level is eventually reached where one encounters the human. Attempts to go further have been motivated, for example, by desires to predict performance of a human in a given task (e. g., lifting an object, assembling a product, etc.) from a set of measures that characterizes the human. At the human-task interface level, difficulty is encountered with regard to the strategy for approaching a system as complex, multifaceted, and multipurpose as a human [Fleishman & Quaintance, 1984; Wickens, 1984]. In essence, the full scope of options that have emerged from the bottom-up development path is now encountered from the opposite direction. Options range from relatively gross analyses (e. g., estimates of the “fraction” of a task that is physical or mental) to those which are much more detailed and quantitative. The daunting prospect of considering a “comprehensive quantitative model” has led to approaches and models, argued to be “more practical,” in which sets of parameters are often selected in a somewhat mysterious fashion based on experience (including previous research) and intuition. The selected parameters are then used to develop predictive models, most of which have been based primarily on statistical methods (i. e., regression models) [Fleishman, 1967; Fleishman & Quaintance, 1984]. Although the basic modeling tools depend only on correlation, it is usually possible to envision a causal link between the independent variables selected (e. g., visual acuity) and the dependent variable to be predicted (e. g., piloting an aircraft). Models (one per task) are then tested in a given population and graded with regard to their prediction ability, the best of which have performed marginally [Kondraske and Beehler, 1994]. Another characteristic associated with many of the statistically based modeling efforts from the noted communities is the almost exclusive use of healthy, “normal” subjects for model development (i. e., humans with impairments were excluded). Homogeneity is a requirement of such statistical models, leading to the need for one model per task per population (at best). Also, working with a mindset that considers only normal subjects can be observed to skew estimates regarding which of the many parameters that one might choose for incorporation in a model are “most important.” The relatively few exceptions that employ cause-and-effect models (e. g., based on physical laws) at some level of fidelity [e. g., Chaffin & Andersonn, 1984] often adopt methods that have emerged from the bottom-up path and are, as noted above, limited in character at the “total human” level (e. g., “biomechanical” in the example cited).

The issue is not that no useful models have emerged from previous efforts but rather that no clear comprehensive strategy has emerged for modeling at the human-task interface level. A National Research Council panel on human performance modeling [Baron et al., 1990] considered the fundamental issues discussed here and also underscored needs for models at the human-task interface level. While it was concluded that an all-inclusive model might be desirable (i. e., high fidelity, in the sense that biomechan­ical, information processing, sensory and perceptual aspects, etc. are represented), such a model was characterized as being highly unlikely to be achieved and perhaps ultimately not useful because it would be overly complex for many applications. The basic recommendation made by this panel was to pursue development of more limited scope submodels. The implication is that two or more submodels could be integrated to achieve a broader range of fidelity, with the combination selected to meet the needs of particular situations. The desire to “divide efforts” due to inherent complexity of the problem also surfaces within the histories of the bottom-up and top-down development paths discussed above. While a rea­sonable concept in theory, one component in the division of effort that has consistently been underrep­resented is the part that ties together the so-called submodels. Without a conceptual framework for integration of relatively independent modeling efforts and a set of common modeling constructs, pros­pects for long-term progress are difficult to envision. This, along with the recognition that enough work had been undertaken in the submodel areas so that key issues and common denominators could be identified, motivated development of the ERM.

The broad objectives of the ERM [Kondraske, 1994] are most like those of Fleishman and colleagues [Fleishman, 1966, 1972,1982; Fleishman & Quaintance, 1984], whose efforts in human performance are generally well known in many disciplines. These are the only two efforts known that (1) focus on the total human in a task situation (i. e., directly address the human-task interface level); (2) consider tasks in general, and not only a specific task such as gait, lifting, reading, etc.; (3) incorporate all aspects of the total human system (e. g., sensory, biomechanical, information processing, etc.); and (4) aim at quantitative models. There are also some similarities with regard to the incorporation of the ideas of “abilities” (of humans) and “requirements” (of tasks). The work of Fleishman and colleagues has thus been influential in shaping the ERM either directly or indirectly through its influence of others. However, there are several substantive conceptual differences that have resulted in considerably different endpoints. Fleishman’s work emerged from “the task” perspective and is rooted in psychology, whereas the ERM Emerges from the perspective of “human system architecture” and is rooted in engineering methodology with regard to quantitative aspects of system performance but also incorporates psychology and physi­ology. Both approaches address humans and tasks, and both efforts contain aspects identifiable with psychology and engineering, as they ultimately must. These different perspectives, however, may explain in part some of the major differences. Aspects unique to the ERM include (1) the use of a resource construct for modeling and measurement of all aspects of a system’s performance, (2) the use of cause – and-effect resource economic principles (i. e., the idea of threshold “costs” for achieving a given level of performance in any given high-level task), (3) the concept of monadology (i. e., the use of a finite set of “elements” to explain a complex phenomenon), and (4) a consistent strategy for identifying performance elements at different hierarchical levels.

The ERM attempts to provide a quantitative and relatively straightforward framework for character­izing the human system, tasks, and the interface of the human to tasks. It depends in large part on, and evolves directly from, a separate body of material referred to collectively as general systems performance theory (GSPT). GSPT was developed first and independently, i. e., removed from the human system context. It incorporates resource constructs exclusively for modeling of the abstract idea of system performance, including specific rules for measuring performance resource availability and resource eco­nomic principles to provide a cause-and-effect analysis of the interface of any system (e. g., humans) to tasks. The concept of a performance model is emphasized and distinguished from other model types.

Basic Principles

The history of the ERM and the context in which it was developed are described elsewhere [Kondraske, 1987a, 1990b, 2000]. It is important to note that the ERM is derived from the combination of GSPT with the philosophy of monadology and their application to the human system. As such, these two constituents are briefly reviewed before presenting and discussing the actual ERM.

General Systems Performance Theory

The concept of performance now pervades all aspects of life, especially decision-making processes that involve both human and artificial systems. Yet it has not been well understood theoretically, and systematic techniques for modeling and its measurement have been lacking. While a considerable body of material applicable to general systems theory exists, the concept of performance has not been incorporated in it, nor has performance been addressed in a general fashion elsewhere. Most of the knowledge that exists regarding performance and its quantitative treatment has evolved within individual application contexts, where generalizations can easily be elusive.

Performance is multifaceted, pertaining to how well a given system executes an intended function and the various factors that contribute to this. It differs from behavior of a system in that “the best of something” is implied. The broad objectives of GSPT are

To provide a common conceptual basis for defining and measuring all aspects of the performance of any system.

To provide a common conceptual basis for the analysis of any task in a manner that facilitates system-task interface assessments and decision making.

To identify cause-and-effect principles, or laws, that explain what occurs when any given system is used to accomplish any given task.

While GSPT was motivated by needs in situations where the human is “the system” of interest and it was first presented in this context [Kondraske, 1987a], application of it has been extended to the context of artificial systems. These experiences range from computer vision and sensor fusion [Yen & Kondraske, 1992] to robotics [Kondraske & Khoury, 1992; Kondraske & Standridge, 1988].

A succint statement of GSPT designed to emphasize key constructs is presented below in a steplike format. The order of steps is intended to suggest how one might approach any system or system-task interface situation to apply GSPT. While somewhat terse and “to-the-point,” it is nonetheless an essential prerequisite for a reasonably complete understanding the ERM.

Within a domain of interest, select any level of abstraction and identify the system(s) of interest (i. e., the physical structure) and its function (i. e., purpose).

Consider “the system” and “the task” separately.

Use a resource construct to model the system’s performance. First, consider the unique intangible qualities that characterize how well a system executes its function. Each of these is considered to represent a unique performance resource associated with a specific dimension of performance (e. g., speed, accuracy, stability, smoothness, “friendliness,” etc.) of that system. Each performance resource is recognized as a desirable item (e. g., endurance versus fatigue, accuracy versus error, etc.) “possessed” by the system in a certain quantitative amount. Thus one can consider quantifying the amount of given quality available. As illustrated, an important consequence of using the resource construct at this stage is that confusion associated with duality of terms is eliminated.

Looking toward the system, identify all I dimensions of performance associated with it. In situa­tions where the system does not yet exist (i. e., design contexts), it is helpful to note that dimensions of performance of the system are the same as those of the task.

5a. Keeping the resource construct in mind, define a parameterized metric for each dimension of performance (e. g., speed, accuracy, etc.). If the resource construct is followed, values will be produced with these metrics that are always nonnegative. Furthermore, a larger numeric value will consistently represent more of a given resource and therefore more performance capacity.

5b. Measure system performance with the system removed from the specific intended task. This is a reinforcement of Step 2. The general strategy is to maximally stress the system (within limits of comfort and/or safety, when appropriate) to define its performance envelope, or more specifically, the envelope that defines performance resource availability, RA (t). Note that RA (t) is a continuous surface in the system’s nonnegative, multidimensional performance space. Also note that unless all dimensions of performance and parameterized metrics associated with each are defined using the resource construct, a performance envelope cannot be guaranteed. Addressing the issue of measurement more specifically, consider resource availability values RA.Qii(t) for i = 1 to I, associated with each of the I dimensions of performance. Here, each Q;>i represents a unique condition, in terms of a set of values Ri along other identified dimensions of performance, under which a specific resource availability (RA.) is measured; i. e., Q;>i = {R^, R2li,0 , Rpi} for all p □ i (1 □ p □ I). The subscript i is used to distinguish several possible conditions under which a given resource availability (RA, for example) can be measured. These values are measured using a set of “test tasks,” each of which is designed to maximally stress the system (within limits of comfort and/or safety, when appropriate): (a) along each dimension of performance individually (where Qi, i = Qi0 = {0,0, □ ,0}) or (b) along selected subsets of dimensions of performance simultaneously (i. e0 Qa = Qn where each possible Qi n has one or more nonzero elements). The points obtained [RA;Iq*(t)] provide the basis to estimate the performance envelope RAs(t). Note that if only on – axis points are obtained (e. g., maximally stress one specific performance resource availability with minimal or no stress on other performance resources, or the Qi0 condition), a rectangular or idealized performance envelope is obtained. A more accurate representation, which would be contained within the idealized envelope, can be obtained at the expense of making additional measurements or the use of known mathematic functions based on previous studies that define the envelope shape in two or more dimensions.

5c. Define estimates of single-number system figures-of-merit, or composite performance capacities, as the mathematical product of all or any selected subset of RA. Q (t). If more accuracy is desired and a sufficient number of data points is available from the measurement process described in Step 5b, composite performance capacities can be determined by integration over RA (t) to determine the volume enclosed by the envelope. The composite performance capacity is a measure of perfor­mance at a higher level of abstraction than any individual dimension of performance at the “system” level, representing the capacity of the system to perform tasks that place demands on those perfor­mance resources availabilities included in the calculation. Different composite performance capacities can be computed for a given system by selecting different combinations of dimensions of perfor­mance. Note that the definition of a composite performance capacity used here preserves dimen­sionality; e. g., if speed and accuracy dimensions are included in the calculation, the result has units of speed □ accuracy. (This step is used only when needed, e. g., when two general-purpose systems of the same type are to be compared. However, if decision making that involves the interface of a specific system to a specific task is the issue at hand, a composite performance capacity is generally of any use only to rule out candidates).

Assess the “need for detail.” This amounts to a determination of the number of hierarchical levels included in the analysis. If the need is to determine if the currently identified “system” can work in the given task or how well it can execute its function, go to Step 7 now. If the need is to determine the contribution of one or more constituent subsystem or why a desired level of performance is not achieved at the system level, repeat Steps 1 to 5 for all J functional units (subsystems), or a selected subset thereof based on need, that form the system that was originally identified in Step 1; i. e., go to the next-lowest hierarchical level.

At the “system” level, look toward the task(s) of interest. Measure, estimate, or calculate demands on system performance resources (e. g., the speed, accuracy, etc. required), RD. iQg (t), where the notation here is analogous to that employed in Step 5b. This represents the quantitative definition and communication of goals, or the set of values PHLT representing level of performance P desired in a specific high-level task (HLT). Use a worst-case or other less-conservative strategy (with due consideration of the impact of this choice) to summarize variations over time. This will result in a set of M points (RDmi for m = 1 to M) that lie in the multidimensional space defined by the set of I dimensions of performance. Typically, M □ I.

Use resource economic principles (i. e., require RA □ RD for “success”) at the system level and at all system-task interfaces at the subsystem level (if included) to evaluate success/failure at each interface. More specifically, for a given system-task interface, all task demand points (i. e., the set of RDm associated with a given task or subtask) must lie within the performance resource envelope RAs (t) of the corresponding system. This is the key law that governs system-task interfaces. If a two-level model is used (i. e., “system” and “subsystem” levels are incorporated), map system-level demands to demands on constituent subsystems. That is, functional relationships between PHLT and demands imposed on constituent subsystems [i. e., RD..(t, PHLT)] must be determined. The nature of these mappings depends on the type of systems in question (e. g., mechanical, information processing, etc.). The basic process includes application of Step 7 to the subtasks associated with each subsystem. If resource utilization flexibility (i. e., redundancy in subsystems of similar types) exists, select the “best” or optimal subsystem Configuration (handled in GSPT with the concept of performance resource substitution) and procedure (i. e., use of performance resources over time) as that which allows accomplishment of goals with minimization of stress on available performance resources across all subsystems and over the time of task execution. Thus redundancy is addressed in terms of a constrained performance resource optimization problem. Stress on individual per­formance resources is defined as 0 < RD. (t, PHLT)/ R^.(t) < 1. It is also useful to define and take note of reserve capacity, i. e., the margin between available and utilized performance resources.

The preceding statement is intended to reflect the true complexity that exists in systems, tasks, and their interfaces when viewed primarily from the perspective of performance. This provides a basis for the judicious decision making required to realize “the best” practical implementation in a given situation where many engineering tradeoffs must be considered. While a two-level approach is described above, it should be apparent that it can be applied with any number of hierarchical levels by repeating the steps outlined in an iterative fashion starting at a different level each time. A striking feature of GSPT is the threshold effect associated with the resource economic principle. This nonlinearity has important impli­cations in quantitative human performance modeling, as well as interesting ramifications in practical applications such as rehabilitation, sports, and education. Note also that no distinction is made as to whether a given performance resource is derived from a human or artificial system; both types of systems, or subcomponents thereof, can be incorporated into models and analyses.


Monadology dates back to 384 B. C. [Neel, 1977] but was formalized and its importance emphasized by Gottfried Wilhelm Leibniz, inventor of calculus, in his text Monadologia in 1714. This text presents what is commonly called Leibniz’s Monadology and has been translated and incorporated into contemporary philosophy texts (e. g., Leibniz and Montgomery, 1992). It is essentially the idea of “basic elements” vis a vis chemistry, alphabets, genetic building blocks, etc. The concept is thus already well accepted as being vital to the systematic description of human systems from certain perspectives (i. e., chemical, genetic). Success associated with previous applications of monadology, whether intentional or unwitting (i. e., discovered to be at play after a given taxonomy has emerged), compels its serious a priori consideration for other problems.

Insight into how monadology is applied to human performance modeling is perhaps more readily obtained with reference to a widely known example in which monadology is evident, such as chemistry. Prior to modern chemistry (i. e., prior to the introduction of the periodic table), alchemy existed. The world was viewed as being composed of an infinite variety of unique substances. The periodic table captured the notion that this infinite variety of substances could all be defined in terms of a finite set of basic elements. Substances have since been analyzed using the “language” of chemistry and organized into categories of various complexity, i. e., elements, simple compounds, complex compounds, etc. Despite the fact that this transition occurred approximately 200 years ago, compounds remain that have yet to be analyzed. Furthermore, the initial periodic table was incorrect and has undergone revision up to relatively recent times. Analogously, in the alchemy of human performance, the world is viewed as being composed of an infinite variety of unique tasks. A “chemistry” can be envisioned that first starts with the identification of the “basic elements” or, more specifically, basic elements of performance. Simple and complex tasks are thus analogous to simple and complex compounds, respectively. The analogy carries over to quantitative aspects of GSPT as well. Consider typical equations of chemical reactions with resources on the left and products (i. e., tasks) on the right. Simple compounds (tasks) are realized by drawing on basic elements in the proper combination and amounts. The amount of “product” (level of performance in a high-level task) obtained depends on availability of the limiting resource.

Another informative aspect of this analogy is the issue of how to deal with the treatment of hierarchical level. Clearly, the chemical elements are made up of smaller particles (e. g., protons, neutrons, and electrons). Physicists have identified even smaller, more elusive entities, such as bosons, quarks, etc. Do we need to consider items at this lowest level of abstraction each time a simple compound such as hydrochloric acid is made? Likewise, the term basic in basic elements of performance is clearly relative and requires the choice of a particular hierarchical level of abstractions for the identification of systems or basic functional units, a level that is considered to be both natural and useful for the purpose at hand. Just as it is possible but not always necessary or practical to map chemical elements down to the atomic particle level, it is possible to consider mapping a basic element of performance (see below) such as elbow flexor torque production capacity down to the level of muscle fibers, biochemical reactions at neuromus­cular junctions, etc.

The Elemental Resource Model

The resource and resource economic constructs used in GSPT specifically to address performance have employed and have become well established in some segments of the human performance field, specif­ically with regard to attention and information processing [Navon & Gopher, 1979; Wickens, 1984]. However, in these cases, the term resource is used mostly conceptually (in contrast to quantitatively), somewhat softly defined, and applied to refer in various instances to systems (e. g., different processing

A Working Model for Human System-Task Interfaces

FIGURE 147.1 The elemental resource model contains multiple hierarchical levels. Performance resources (i. e., the basic elements) at the “basic element level” are finite in number, as dictated by the finite set of human subsystems and the finite set of their respective dimensions of performance. At higher levels, new “systems” can be readily created by configuration of systems at the basic element level. Consequently, there are an infinite number of performance resources (i. e., higher-level elements) at these levels. However, rules of general systems performance theory (refer to text) are applied at any level in the same way, resulting in the identification of the system, its function, dimensions of performance, performance resource availabilities (system attributes), and performance resource demands (task attributes).

Centers), broad functions (e. g., memory versus processing), and sometimes to infer a particular aspect of performance(e. g., attentional resources). In the ERM, through the application of GSPT, these con­structs are incorporated universally (i. e., applied to all human subsystems) and specifically to model “performance” at both conceptual and quantitative levels. In addition to the concept of monadology, the insights of others [Shoner & Kelso, 1988, Turvey et al., 1978] were valuable in reinforcing the basic “systems architecture” employed in the ERM and in refining description of more subtle, but important aspects.

As illustrated iN Fig. 147.1, tHe ERM contains multiple hierarchical levels. Specifically, three levels are defined: (1) the basic element level, (2) the generic intermediate level, and (3) the high level. GSPT is to define performance measures at any hierarchical level. This implies that to measure performance, one must isolate the desired system and then stress it maximally along one dimension of performance (or more, if interaction effects are desired) to determine performance resource availability. For example, consider the human “posture stabilizing” system (at the generic intermediate level), which is stressed maximally along a stability dimension. As further illustrated below, the basic element level represents measurable stepping stones in the human system hierarchy between lower-level systems (i. e., ligaments, tendons, nerves, etc.) and higher-level tasks.

A summary representation emphasizing the basic element level of the ERM is depicted in Fig. 147.2. While this figure is intended to be more or less self-explanatory, a brief walk-through is warranted.

Looking Toward the Human. The entire human (lower portion of Fig. 147.2) is modeled as a pool of elemental performance resources that are grouped into one of four different domains: (1) life-sustaining, (2) environmental interface (containing purely sensory and sensorimotor components), (3) central processing, and (4) information. Within each of the first three domains, physical subsystems referred to as functional units are identified (see labels along horizontal aspect of grids) through application of fairly rigorous criteria [Kondraske, 1990b]. GSPT is applied to each functional unit, yielding first a set of dimensions of performance (defined using a resource construct) for each unit. A single basic element of performance (BEP) is defined by specifying two items: (1) the basic functional unit and (2) one of its dimensions of performance. Within a domain, not every dimension of performance indicated in Fig. 147.2 is applicable to every functional unit in that domain. However, there is an increasing degree of “likeness” among functional units (i. e., fewer fundamentally different types) in this regard as one moves from life-sustaining to environmental-interface to central-processing domains. The fourth domain, the information domain, is substantially different than the other three. Whereas the first three represent physical systems and their intangible performance resources, the information domain simply represents information. Thus, while memory functional units are located within the central-processing domain, the contents of memory (e. g., motor programs and associated reference information) are parti­tioned into the information domain. As illustrated, information is grouped, but within each group there are many specific skills. The set of available performance resources [Ra j(t)ig] consist of both BEPs (i = dimension of performance, j = functional unit) and information sets (e. g., type i within group j). Although intrinsically different, both fit the resource construct. This approach permits even the most abstract items such as motivation and friendliness to be considered with the same basic framework as strength and speed.

Note that resource availability in GSPT and thus in the ERM is potentially a function of time, allowing quantitative modeling of dynamic processes such as child development, aging, disuse atrophy, and rehabilitation. The notation further implies that availability of a given resource must be evaluated at a specific operating point, denoted as Q. At least conceptually, many parameters can be used to characterize this Q point. In general, the goal of measurement when looking “toward the human” is to isolate functional units and maximally stress (safely) individual performance resources to determine availability. Such measures reflect performance capacities. The simplest Q point is one in which there is stress along only one dimension of performance (i. e., that corresponding to the resource being stressed). Higher­fidelity representation is possible at the expense of additional measurements. The degree to which isolation can be achieved is of practical concern in humans. Nonetheless, it is felt that reasonable isolation can be achieved in most situations [Kondraske, 1990a, 1990b]. Moreover, this and similar issues of practical concern should be addressed as separate problems; i. e., they should not be permitted to obfuscate or thwart efforts to explain phenomenon at the human-task interface.

Looking Toward the Task. The midportion of Fig. 147.2 suggests the representation of any given task in terms of the unique set of demands [RD. (t)] imposed on the pool of BEPs and information resources;

E., this is the elemental level of task representation. Shading implies that demands can be represented quantitatively in terms of amount. The upper portion of the figure defines hierarchical mapping options, where mapping is the process of translating what happens in tasks typically executed by humans to the elemental level. Two such additional levels (for a total of three, including the elemental level) are included as part of the ERM: (1) generic intermediate-level tasks and (2) higher-level complex tasks. At all three levels of tasks (Figs. 147.1 aNd 147.2) are processes that occur over time and can be characterized by specific goals (e. g., in terms of speed, force, etc.) and related to systems at the same level that possess performance resources. Using established analytical techniques, even the most complex task can be divided into discrete task segments. Then mapping analyses, which take into account task procedures





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FIGURE147.2 A summary of the major constructs of the elemental resource model, emphasizing the categorization of performance resources at the basic element level into four life-sustaining, environmental-interface, central-processing, and information domains.

(e. g., a squatting subject with two hands on the side of a box), are applied to each task segment to determine Rd. (t). Once this is found, the worst case or a selected percentile point in the resource demand distribution (over a time period corresponding to a selected task segment) can be used to obtain a single numeric value representation of demand for a given resource and the conditions under which the given demand occurs; i. e., the QDpoint (e. g., at what speed and position angle does the worst-case demand on elbow flexor torque occur?). This reduction process requires parameterization algorithms that are similar to those used to process time-series data collected during tests designed to measure performance resource availability.

The Human-Task Interface. Using GSPT, success in achieving the goals of a given task segment is governed by resource economic principles requiring that R^ (j)!q □ RD..(t) for all i and j (i. e., R^ □ RDn AND RA12 □ RD12 AND RA13 □ RD13). In other words, all task demands, when translated to the individual subsystems involved, must fit within the envelopes that define performance resource availability. Adequacy associated with any one resource is a necessary but not sufficient condition for success. Concepts and observations in human performance referred to as compensation or redundancy are explained in terms of resource utilization flexibility, which includes the possibility of substituting one performance resource (of the same dimensionality) for another (i. e., resource substitution). It has been hypothesized [Kondraske, 1990 b] that an optimal performance resource utilization is achieved through learning. Furthermore, the optimization rule suggested by GSPT is that the human system is driven to accomplish task goals and use procedures that minimize performance resource stress (i. e., the fraction of available performance resource utilized) over the duration of a given task segment and across all BEPs involved. Minimizing stress is equivalent to maximizing the margin between available and utilized performance resources. Thus optimization is highly dependent on the resource availability profile. It would be predicted, for instance, that two individuals with different resource availability profiles would not optimally accomplish the same task goals by using identical procedures.

Application Issues

Implications of the ERM and what it demonstrates regarding intrinsic demands imposed by nature and methods for creatively navigating these demands over both the short and long terms are considered. The ERM offers a number of flexibilities with regard to how it can be applied (e. g., “in whole” or “in part,” “conceptually” or “rigorously,” with “low tech” or sophisticated “high tech” tools, and to define perfor­mance measures or to develop predictive models). While immediate application is possible at a conceptual level of application, it also provides the motivation and potential to consider coordinated, collaborative development of sophisticated tools that allow rigorous and efficient solutions to complex problems by practitioners without extraordinary training.

The ERM provides a basis for obtaining insight into the nature of routine tasks that clinicians and other practitioners are expected to perform; there are both “troublesome” and “promising” insights in this regard. Perhaps the most obvious troublesome aspect is that the ERM makes it painfully evident that many BEPs are typically called into play in tasks of daily living, work, and/or recreation and that resource insufficiency associated with any one of this subset of BEPs can be the factor that limits performance in the higher-level task. The further implication is that for rigorous application, one must know (via measurement and analyses) the availability and demand associated with each and every one of these unique resources. An additional complexity with which practitioners must cope is the high degree of specificity and complexity of resources in the information domain (i. e., the “software”). There is no simple, rapid way to probe this domain to determine if the information required for a given task is correct; it requires methods analogous to those used to debug software source code.

Aspects that hold promise are associated with (1) the nature of hierarchical systems, (2) the threshold mathematics of resource economics, and (3) the fact that when n resources combine to address a single task, the mathematics of logical combination is employed to arrive at an overall assessment. That is, the individual “RA □ RD?” questions result in a set of “OK” or “not OK” results that are combined with logical AND operations to obtain the final “OK” or “not OK” assessment (note that the OR operator is used when resource substitution is possible).

Conceptual, Low-Tech Practical Application

The ERM description alone can be used simply to provide a common conceptual basis for discussing the wide range of concepts, measurements, methods, and processes of relevance in human performance or a particular application area [Frisch, 1993; Mayer & Gatchel, 1988; Syndulko et al., 1988]. It also can be used at this level as a basis for structured assessment [Kondraske, 1988b, 2000] of individuals in situations, including therapy prescription, assistive device prescription, independent living decision mak­ing (e. g., self-feeding, driving, etc.), age or gender discrimination issues in work or recreational tasks, etc. At points in such processes, it is often more important to consider the full scope of different performance resources involved in a task using even a crude level of quantification than it is to consider just a select few in great depth. A “checklist” approach is recommended. The professional uses only his or her judgment and experience to consider both the specific individual and the specific task of interest to make quantitative but relatively gross assessments of resource adequacy using a triage-like categori­zation process (e. g., with “definitely limiting,” “definitely not limiting,” or “not sure” categories). This is feasible because of the threshold nature of the system-task interface; in cases where RA and RD are widely separated instrumented, high – resolution measurements are not required to determine if a given perfor­mance resource is limiting. Any resource(s) so identified as “definitely limiting” becomes an immediate focus of interest. If none is categorized as such, concern moves to those in the “not sure” category, in which case more sensitive measurements may be required. Purely subjective methods of measuring resource availability and/or demands can be augmented with selected, more objective and higher-reso- lution measurements in “hybrid applications.”

Conceptual, Theoretical Application

The ERM can be used to reconsider previous work in human performance. For example, it can be employed to reason why Fleishman [Fleishman & Quaintance, 1984] (as well as others) achieved prom­ising, but limited, success with statistically based predictions of performance in higher-level tasks using regression models with independent variables, which can now be viewed as representing lower-level performance resources (in most cases). Specifically, regression models rely heavily on an assumption that there exists some correlation between dependent and each independent variable, the latter of which typically represent scores from maximal performance tasks and therefore reflect resource availability (using GSPT and ERM logic). Brief reflection results in the realization that correlation is not to be anticipated between the level of performance attainable in a “higher-level task” and availability of one of the many performance resources essential to the task (e. g., if 4 cups of flour are needed for a given cake, having 40 cups available will not alone result in a larger cake of equal quality—availability of another ingredient may in fact be limiting). Rather, correlation is expected between high-level task performance and the amount of resource utilization. Unfortunately, as noted above, the independent variables used typically reflect resource availability. The incomplete labeling of performance variables in such studies reflects the general failure to distinguish between utilization and availability. Why not, then, just use measures of resource utilization in such statistical models? Resource availability measures are simple to obtain in the laboratory without requiring that the individual execute the high-level task of interest. Resource utilization measures can only be obtained experimentally by requiring the subject to execute the task in question, which is counterproductive with respect to the goal of using a set of laboratory measurements to extrapolate to performance in one or more higher-lever task situations. Furthermore, regression models based on linear combination of resource availability measures do not reflect the nonlinear threshold effect accounted for with resource economic, GSPT-based performance models. One potential alternative based on GSPT and termed nonlinear causal resource analysis has been proposed [Kondraske, 1988a; Vasta & Kondraske, 1994].

Application “in Part"

In this approach, whole domains (i. e., many BEPs) are assessed simultaneously, resulting in an estimate or well-founded assumption that states that “all performance resources in domain X are nonlimiting in task(s) Y” Such assumptions are often well justified. For example, it would be reasonable to assume that a young male with a sports-related knee injury would have only a reduction in performance resource availability in the environmental interface domain. More specifically, it is reasonable to assume that the scope of interest can be confined to a smaller subset of functional units, as in gait or speech. These can then be addressed with rigorous application. Examples of this level and manner of applying the ERM have been or are being developed for head/neck control in the context of assistive communication device prescription [Carr, 1989], workplace design [Kondraske, 1988c], evaluating work sites and individuals with disabilities for employment [Parnianpour & Marras, 1993], gait [Carollo & Kondraske, 1987], measurement of upper extremity motor control [Behbehani et al., 1988], speech production performance [Jafari, 1989; Jafari et al., 1989], and to illustrate changes in performance capacity associated with aging [Kondraske, 1989]. Additionally, in some applications only the generic intermediate level need be con­sidered. For example, one may only need to know how well an individual can walk, lift, etc. While it is sometimes painfully clear just how complex the execution of even a relatively simple task is, it also can be recognized that relatively simple, justifiable, and efficient strategies can be developed to maintain a reasonable degree of utility in a given context.

Rigorous, High-Tech Application

While this path may offer the greatest potential for impact, it also presents the greatest challenge. The ultimate goal would be to capture the analytic and modeling capability (as implied by the preceding discussions) for a “total human” (single subject or populations) and “any task” in a desktop computer system (used along with synergistic “peripherals” that adopt the same framework, such as measurement tools [e. g., Kondraske, 1990a]). This suggests a long-term, collaborative effort. However, intermediate tools that provide significant utility are feasible and needed (e. g., [Allard, Stokes, and Blanchi, 1994; Vasta and Kondraske, 1995]). A promising example based directly on GSPT and the ERM is Nonlinear Causal Resource Analysis, or NCRA [Vasta and Kondraske, 1994; Kondraske et al, 1997]. This inferential method has recently been used [Kondraske et al, 1997] to develop models that relate performance resource demands on various human subsystems to level of performance attained in higher level mobility tasks (e. g., gait and stair climbing). In turn, these models have been used to predict level of performance in these higher level tasks with success that far exceeds that which has been obtained with regression models. Furthermore, the NCRA method inherently provides not only a prediction of high level task performance, but also identifies which performance resources are most likely to be preventing better performance (i. e., which ones are the “limiting performance resources”). While tools that are useful to practitioners are desirable, they are almost essential for the efficient conduct of in-depth experimental work with the ERM that must accompany the evolution of such tools.

The issue of biologic variability and its influence on numeric analyses can be raised in the context of rigorous numeric application. In this regard, the methods underlying GSPT and the ERM (or any similar cause-and-effect model) are noted to be analogous with those used to design artificial systems. In recent years, conceptual approaches and mathematic tools widely known as Taguchi methods [e. g., Bendell et al., 1988] have been shown to be effective for understanding and managing a very similar type of variability that surfaces in the manufacture of artificial systems (e. g., variability associated with performance of components of larger systems and the effect on aspects of performance of the final “product”). Such tools may prove useful in working through engineering problems such as those associated with variability.


The ERM is a step toward the goal of achieving an application-independent approach to modeling any human-task interface. It provides a systematic and generalizable (across all subsystem types) means of identifying performance measures that characterize human subsystems, as well as a consistent basis for performance measurement definition (and task analysis). It also has served to stimulate focus on a standardized, distinct set of variables that facilitates clear communication of an individual’s status among professionals.

After the initial presentation, refinements in both GSPT and the ERM were made. However, the basic approaches, terminology, and constructs used in each have remained quite stable. More recent work has focused on development of various components required for application of the ERM in nontrivial situations. This entails using GSPT and basic ERM concepts to guide a full “fleshing out” of the details of measurement parameterizations and models for different types of human subsystems, definition of standard conventions and notations, and development of computer-based tools. In addition, experimen­tal studies designed to evaluate key constructs of the ERM and to demonstrate the various ways in which it can be applied are being conducted. A good portion of the developmental work is aimed at building a capability to conduct more complex, nontrivial experimental studies. Collaborations with other research groups also have emerged and are being supported to the extent possible. Experiences with it in various contexts and it various levels of application have been productive and encouraging.

The ERM is one, relatively young attempt at organizing and dealing with the complexity of some major aspects of human performance. There is no known alternative that, in a specific sense, attempts to accomplish the same goals as the working model presented here. Is it good enough? For what purposes? Is a completely different approach or merely refinement required? The process of revision is central to the natural course of the history of ideas. Needs for generalizations in human performance persist.

Defining Terms

Basic element of performance (BEP): A modeling item at the basic element level in the ERM defined

By identification of a specific system at this level and one of its dimensions of performance (e. g., functional unit = visual information processor, dimension of performance = speed, BEP = visual information processor speed).

Behavior: A general term that relates to what a human or artificial system does while carrying out its

Function(s) under given conditions. Often, behavior is characterized by measurement of selected parameters or identification of unique system states over time.

Composite performance capacity: A performance capacity at a higher level of abstraction, formed by

Combining two or more lower-level performance capacities (e. g., via integration to determine the area or volume within a performance envelope).

Dimension of performance: A unique quality that characterizes how well a system executes its function

(e. g., speed, accuracy, torque production); one of axes or the label associated with one of the axes in a multidimensional performance space.

Function: The purpose of a system. Some systems map to a single primary function (e. g., process visual

Information). Others (e. g., the human arm) map to multiple functions, although at any given time multifunction systems are likely to be executing a single function (e. g., polishing a car). Functions can be described and inventoried, whereas level of performance of a given function can be mea­sured.

Generic intermediate level: One of three major hierarchical levels for systems and tasks identified in

The elemental resource model. The generic intermediate level represents new systems (e. g., postural maintenance system, object gripper, object lifter, etc.) formed by the combination of functional units at the basic element level (e. g., flexors, extensors, processors, etc.). The term generic is used to imply the high frequency of use of systems at this level in tasks of daily life (i. e., items at the “high level” in the ERM).

Goal: A desired endpoint (i. e., result) typically characterized by multiple parameters, at least one of

Which is specified. Examples include specific task goals (e. g., move an object of specified mass from point A to point B in 3 seconds) or estimated task performance (maximum mass, range, speed of movement obtainable given a specified elemental performance resource availability profile), depending on whether a reverse or forward analysis problem is undertaken. Whereas function describes the general process of a task, the goal directly relates to performance and is quantitative.

Limiting resource: A performance resource at any hierarchical level (e. g., vertical lift strength, knee

Flexor speed) that is available in an amount that is less than the worst-case demand imposed by a task. Thus a given resource can only be “limiting” when considered in the context of a specific task.

Performance: Unique qualities of a human or artifical system (e. g., strength, speed, accuracy, endur­

Ance) that pertain to how well that system executes its function.

Performance capacity: A quantity in finite availability that is possessed by a system or subsystem,

Drawn on during the execution of tasks, and limits some aspect (e. g., speed, force, production, etc.) of a system’s ability to execute tasks; or, the limit of that aspect itself.

Performance envelope: The surface in a multidimensional performance space, formed with a selected

Subset of a system’s dimensions of performance, that defines the limits of a system’s performance. Tasks represented by points that fall within this envelope can be performed by the system in question.

Performance resource: A unique quality of a system’s performance modeled and quantified using a

Resource construct.

Performance resource substitution: The term used in GSPT to describe the manner in which intelligent

Systems, such as humans, utilize redundancy or adapt to unusual circumstances (e. g., injuries) to obtain optimal procedures for executing a task.

Procedure: A set of constraints placed on a system in which flexibility exists regarding how a goal (or set

Of goals) associated with a given function can be achieved. Procedure specification requires specifi­cation of initial, intermediate, and/or final states or conditions dictating how the goal is to be accomplished. Such specification can be thought of in terms of removing some degrees of freedom.

Resource construct: The collective set of attributes that define and uniquely characterize a resource.

Usually, the term is applied only to tangible items. A resource is desirable and measurable in terms of amount (from zero to some finite positive value) in such a manner that a larger numeric value indicates a greater amount of the resource.

Resource economic principle: The principle, observable in many contexts, that states that the amount

Of a given resource that is available (e. g., money) must exceed the demand placed on it (e. g., cost of an item) if a specified task (e. g., purchase of the item) is to be executed.

Resource utilization flexibility: A term used in GSPT to describe situations in which there are more

Than one possible source of a given performance resource type, i. e., redundant supplies exist.

Structure: Physical manifestation and attributes of a human or artificial system; the object of one type

Of measurements at multiple hierarchical levels.

Style: Allowance for variation within a procedure, resulting in the intentional incomplete specification

Of a procedure or resulting from either intentional or unintentional incomplete specification of procedure.

System: A physical structure, at any hierarchical level of abstraction, that executes one or more functions.

Task: That which results from (1) the combination of specified functions, goals, and procedures or

(2) the specification of function and goals and the observation of procedures utilized to achieve the goals.


Allard P, Stokes IAF, Blanchi JP. 1994 Three-Dimensional Analysis of Human Movement. Champaign, Ill, Human Kinetics.

Baron S, Kruser DS, Huey BM (eds). 1990. Quantitative Modeling of Human Performance in Complex, Dynamic Systems. Washington, National Academy Press.

Behbehani K, Kondraske GV, Richmond JR. 1988. Investigation of upper extremity visuomotor control performance measures. IEEE Trans Biomed Eng 35(7):518.

Bendell A, Disney J. Pridmore WA. 1988. Taguchi Methods: Applications in World Industry. London, IFS Publishing.

Botterbusch KF. 1987. Vocational Assessment and Evaluation Systems: A Comparison. Menomonie, Wisc, Stout Vocational Rehabilitation Institute, University of Wisconsin.

Card SK, Moran TP, Newell A. 1986. The model human processor. In KR Boff, L Kaufman, and JP Thomas (eds), Handbook of Perception and Human Performance, vol II, pp 45.1-45.35. New York, Wiley.

Carollo JJ, Kondraske GV. 1987. The prerequisite resources for walking: Characterization using a task analysis strategy. In J Leinberger (ed), Proceedings of the Ninth Annual IEEE Engineering and Medical Biology Society Conference, p 357.

Carr B. 1989. Head/Neck Control Performance Measurement and Task Interface Model. M. S. thesis, University of Texas at Arlington, Arlington, Texas.

Chaffin DB, Andersson GBJ. 1984. Occupational Biomechanics. New York, Wiley.

Delp SL, Loan JP, Hoy MG, et al. 1990. An interactive graphics-based Model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans Biomed Eng 37(8):757.

Fleishman EA. 1956. Psychomotor selection tests: Research and application in the United States Air Force. Personnel Psychology 9:449.

Fleishman EA. 1966. Human abilities and the acquisition of skill. In EA Bilodeau (ed), Acquisition of Skill, pp 147-167. New York, Academic Press.

Fleishman EA. 1967. Performance assessment based on an empirically derived task taxonomy. Human Factors 9:349.

Fleishman EA. 1972. Structure and measurement of psychomotor abilities. In RN Singer (ed), The Psychomotor Domain: Movement Behavior, pp 78-106. Philadelphia, Lea and Febiger.

Fleishman EA. 1982. Systems for describing human tasks. Am Psychol 37:821.

Fleishman EA, Quaintance MK., 1984. Taxonomies of Human Performance. New York, Academic Press.

Frisch HP. 1993. Man/machine interaction dynamics and performance analysis. In Proceedings of the NATO-Army-NASA Advanced Study Institute on Concurrent Engineering Tools and Technologies for Mechanical System Design. New York: Springer-Verlag.

Gilbreth FB, Gilbreth FM. 1917. Applied Motion Study. New York, Sturgis and Walton.

Gottlieb GL, Corcos DM, Agarwal GC. 1989. Strategies for the control of voluntary movements with one mechanical degree of freedom. Behav Brain Sci 12:189.

Hemami H. 1988. Modeling, control, and simulation of human movement. CRC Crit Rev Bioeng 13(1):1.

Jafari M. 1989. Modeling and Measurement of Human Speech performance Toward Pathology Pattern Recognition. Ph. D. dissertation, University of Texas at Arlington, Arlington, Texas.

Jafari M, Wong KH, Behbehani K, Kondraske GV. 1989. Performance characterization of human pitch control system: An acoustic approach. J Acous Soc Am 85(3):1322.

Kondraske GV. 1987«. Human performance: Science or art? In K Foster (ed), Proceedings of the Thir­teenth Northeast Bioengineering Conference, pp 44-47.

Kondraske GV. 1987 b. Looking at the study of human performance. SOMA: Eng Human Body (ASME) 2(2):50.

Kondraske GV. 1988«. Experimental evaluation of an elemental resource model for human performance. In G Harris, C Walker (eds), Proceedings of the Tenth Annual IEEE Engineering and Medical Biology Society Conference, pp 1612-1613. New York, IEEE.

Kondraske GV. 1988b. Human performance measurement and task analysis. In A Enders (ed), Technology for Independent Living Sourcebook, 2d ed. Washington, RESNA.

Kondraske GV. 1988c. Workplace design: An elemental resource approach to task analysis and human performance measurements. In Proceedings of the International Conference of the Association of Advanced Rehabilitation Technology, pp 608-611.

Kondraske GV. 1989. Neuromuscular performance: Resource economics and product-based composite indices. In Proceedings of the Eleventh Annual IEEE Engineering and Medical Biology Society Conference, pp 1045-1046. New York, IEEE.

Kondraske GV. 1990«. A PC-based performance measurement laboratory system. J Clin Eng 15(66):467.

Kondraske GV. 1990 b. Quantitative measurement and assessment of performance. In RV Smith, JH Leslie (eds), Rehabilitation Engineering, pp 101-125. Boca Raton, Fla, CRC Press.

Kondraske GV. 1993. The HPI Shorthand Notation System for Human System Parameters. Technical Report 92-001R V1.5. Human Performance Institute, University of Texas at Arlington, Arlington, Texas.

Kondraske GV. 1994. An elemental resource model for the human-task interface. Int J Technol Assess Health Care, 11(2), 153-173.

Kondraske GV. 2000. Measurement tools and processes in rehabilitation engineering. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Kondraske GV, Beehler PJH. 1994. Applying general systems performance theory and the elemental resource model to gender-related issues in physical education and sport. Women Sport Phys Act J 3(2)1-19.

Kondraske GV, Beehler PJH, Behbehani K, et al. 1988. Measuring human performance: Concepts, meth­ods, and application examples. SOMA: Eng Human Body 2 (ASME), Jan 6.

Kondraske GV, Johnston, C., Pearson, A., and Tarbox, L. 1997. Performance Prediction and limiting resource identification with nonlinear causal resource analysis. Proceedings, 19th Annual Engineering in Medicine and Biology Society Conference,(pp. 1813-1816).

Kondraske GV, Khoury GJ. 1992. Telerobotic system performance measurement: Motivation and meth­ods. In Cooperative Intelligent Robotics in Space III, PP 161-172. New York, SPIE.

Kondraske GV, Standridge R. 1988. Robot performance: conceptual strategies. In Conference of Digest IEEE Midcon/88 Technical Conference, pp 359-362. New York, IEEE.

Leibniz, G. W. and Montgomery, G. R. Discourse on Metaphysics and the Monadology Prometheus Books, 1992.

Mayer TG, Gatchel RJ. 1991. Functional Restoration for Spinal Disorders: The Sports Medicine Approach, pp 66-77. Philadelphia, Lea & Febinger.

Meister D. 1989. Conceptual Aspects of Human Performance. Baltimore, Johns Hopkins University Press.

Navon D, Gopher D. 1979. On the economy of the human processing system. Psych Rev 96:214.

Neel A. 1977. Theories of Psychology: A Handbook. Cambridge, Mass, Schenkman.

Panianpour M, Marras WS. 1993. Development of clinical protocols based on ergonomics evaluation in response to American Disability Act (1992). In Rehabilitation Engineering Center Proposal to National Institute on Disability and Rehabilitation Research, Ohio State University, Columbus, Ohio.

Schoner G, Kelso JAS. 1988. Dynamic pattern generation in behavioral and neural systems. Science 239:1513.

Syndulko K, Tourtellotte WW, Richter E. 1988. Toward the objective measurement of central processing resources. IEEE Eng Med Biol Soc Magazine 7(1):17.

Taylor FW. 1911. The Principles of Scientific Management. New York, Harper and Brothers.

Turvey, MT, Shaw RE, Mace W. 1978. Issues in the theory of action: Degrees of freedom, coordinative structures, and coalitions, In J Requin (ed), Attention and Performance VII. Hillsdale, NJ, Lawrence Earlbaum.

US Department of Labor. 1992. Dictionary of Occupation Titles, 4th ed. Baton Rouge, La, Claitor’s Publishing Division.

Vasta PJ, Kondraske GV. 1994. Performance prediction of an upper extremity reciprocal task using non­linear causal resource analysis. In Proceedings of the Sixteenth Annual IEEE Engineering in Med­icine and Biology Society Conference. New York, IEEE.

Wickens CD. 1984. Engineering Psychology and Human Performance. Columbus, Ohio, Charles E Merrill.

World Health Organization (WHO). 1980. International classification of impairments, disabilities, and handicaps. WHO Chron 34:376.

Yen SS, Kondraske GV. 1992. Machine shape perception: Object recognition based on need-driven resolution flexibility and convex-hull carving. In Proceedings of the Conference on Intelligent Robots and Computer Vision X: Algorithm Techniques, pp 176-187. New York, SPIE.

Further Information

General discussions of major issues associated with human performance modeling can be found in the

Following texts:

Fleishman EA, Quaintance MK. 1984. Taxonomies of Human Performance. New York, Academic Press.



Requin J. 1978. Attention and Performance VII. Hillsdale, NJ, Lawrence Earlbaum.


More detailed information regarding general systems performance theory, the elemental resource model,

And their application is available from the Human Performance Institute, P. O. Box 19180, University of

Texas at Arlington, Arlington, TX 76019-0180 and at the HPI web site: Http://www-ee. uta. edu/hpi.

Smith, S. S. “Measurement of Neuromuscular Performance Capacities.” The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000

Rehabilitation Engineering Technologies: Principles of Application

The Conceptual Frameworks

The Provision Process

The Shifting Paradigm • The Evaluation • Service Delivery Models

Education and Quality Assurance RESNA

Douglas Hobson

University of Pittsburgh

Elaine Trefler

University of Pittsburgh

подпись: douglas hobson
university of pittsburgh
elaine trefler
university of pittsburgh
Specific Impairments and Related Technologies Mobility • Sitting • Sensation • Access (Person-Machine Interface) • Communication • Transportation •

Activities of Daily Living (ADL) • School and Work • Recreation • Community and Workplace Access

Future Developments

Rehabilitation engineering is the branch of biomedical engineering that is concerned with the application of science and technology to improve the quality of life of individuals with disabilities. Areas addressed within rehabilitation engineering include wheelchairs and seating systems, access to computers, sensory aids, prosthetics and orthotics, alternative and augmentative communication, home and work-site mod­ifications, and universal design. Because many products of rehabilitation engineering require careful selection to match individual needs and often require custom fitting, rehabilitation engineers have necessarily become involved in service delivery and application as well as research, design, and develop­ment. Therefore, as we expand on later, it is not only engineers that practice within the field of rehabil­itation engineering.

As suggested above, and as in many other disciplines, there are really two career tracks in the field of rehabilitation engineering. There are those who acquire qualifications and experience to advance the state of knowledge through conducting research, education, and product development, and there are others who are engaged in the application of technology as members of service delivery teams. At one time it was possible for a person to work in both arenas. However, with the explosion of technology and the growth of the field over the past decade, one must now specialize not only within research or service delivery but often within a specific area of technology.

One can further differentiate between rehabilitation and assistive technology. Rehabilitation technology is a term most often used to refer to technologies associated with the acute-care rehabilitation process. Therapy evaluation and treatment tools, clinical dysfunction measurement and recording instrumentation,
And prosthetic and orthotic appliances are such examples. Assistive technologies are those devices and services that are used in the daily lives of people in the community to enhance their ability to function independently, examples being specialized seating, wheelchairs, environmental control devices, worksta­tion access technologies and services are now communication aids. Recognition and support of assistive technology devices and services are now embedded in all the major disability legislation that has been enacted over the last decade.

The primary focus of this chapter is on the role of the rehabilitation engineering practitioner as he or she carries out the responsibilities demanded by the application of assistive technology.

Before launching into the primary focus of this chapter, let us first set a conceptual framework for the raison d’etre for assistive technology and the role of the assistive technology professional.

The Conceptual Frameworks

The application of assistive technology can be conceptualized as minimizing the functional gap between the person and his or her environment. This reality is what technology does for all of us to varying degrees. For example, if you live in a suburban area that has been designed for access only by car and your car breaks down, you are handicapped. If your house has been designed to be cooled by air conditioning in the "dog days" of summer and you lose a compressor, your comfort is immediately compromised by your incompatibility with your environment. Similarly, if you live in a home that has only access by steps and you have an impairment requiring the use of a wheelchair, you are handicapped because you no longer have abilities that are compatible with your built environment. Because our environments, homes, workplaces, schools, and communities have been designed to be compatible with the abilities of the norm, young children, persons with disabilities, and many elderly people experience the consequences of their mismatch as a matter of course. The long-term utopian solution would be to design environments and their contents so that they can be used by all people of all ages, which is the essence of the universal design concept. However, given that today we do not have very many products and environments that have been universally designed, rehabilitation engineers attempt to minimize the effects of the mismatch by designing, developing, and providing technologies that will allow persons with disabilities to pursue their life goals in a manner similar to any other person. Of course, the rehabilitation engineer cannot accomplish this working in isolation but rather must function as a part of a consumer – responsive team that can best deal with the multiplicity of factors that usually impact on the successful application of assistive technology.

Let us now move to another conceptual framework, one that conceptualizes how people actually interact with technology.

The following conceptualization has been adapted from the model proposed by Roger Smith [Smith, 1992]. In Fig. 146.1, Smith suggests that there are three cyclic elements that come into play when humans interact with technology: the human and his or her innate sensory, cognitive, and functional abilities; the human factor’s characteristics of the interface between the human and the technology; and the technical characteristics of the technology itself in terms of its output as a result of a specific input by the user. People with disabilities may have varying degrees of dysfunction in their sensory, cognitive, and functional abilities. The interface will have to be selected or adapted to these varying abilities in order to allow the person to effectively interact with the technology. The technology itself will need to possess specific electronic or mechanical capabilities in order to yield the desired outcome. The essence of assistive technology applications is to integrate all three of these element into a functional outcome that meets the specific needs of a user. This is usually done by selecting commercially available devices and tech­nologies at a cost that can be met by either the individual or his or her third-party payment source. When technologies are not available, then they must be modified from existing devices or designed and fabricated as unique custom solutions. It is particularly in these latter activities that a rehabilitation engineer can make his or her unique contribution to team process.

Rehabilitation Engineering Technologies: Principles of Application

FIGURE 146.1 Conceptual framework of technology and disability. (Modified from Smith [1992].)

подпись: figure 146.1 conceptual framework of technology and disability. (modified from smith [1992].)

Environment/Technology Application.

подпись: environment/technology application .In 1995, Cook and Hussey [Cook and Hussey, 1995], published an excellent text, Assistive Technolo­gies—Principles and Practice. As well as comprehensively addressing many of the assistive technologies
Briefly covered in this chapter, they also present a conceptual framework which builds on the one developed by Smith above. They introduce the additional concepts of activity and context. That is, understanding of a person’s activity desires and the context (social, setting, physical) in which they are to be carried out are essential components to successful assistive technology intervention.

It should be realized that there are several levels of assistive technology. The first level might be termed fundamental technology in contrast to advanced technology. Fundamental technologies, such as walkers, crutches, many wheelchairs, activities of daily living (ADL) equipment, etc., usually do not require the involvement of the rehabilitation engineer in their application. Others on the team can better assess the need, confirm the interface compatibility, and verify that the outcome is appropriate. The rehabilitation engineer is most often involved in the application of advanced technologies, such as powered wheelchairs, computerized workstation designs, etc., that require an understanding of the underlying technological principles in order to achieve the best match with the abilities and needs of the user, especially if custom modifications or integration of devices are required to the original equipment. The rehabilitation engineer is usually the key person if a unique solution is necessary.

Let’s now discuss a few fundamental concepts related to the process by which assistive technology is typically provided in various service delivery programs.

The Provision Process The Shifting Paradigm

In the traditional rehabilitation model of service delivery, a multidisciplinary team of professionals is already in place. Physicians, therapists, counselors, and social worker meet with the client and, based on the findings of a comprehensive evaluation, plan a course of action. In the field of assistive technology, the rules of the team are being charted anew. First, the decision making often takes place in a nonmedical environment and often without a physician as part of the team. Second, the final decision is rapidly moving into the hands of the consumer, not the professionals. The third major change is the addition of a rehabilitation engineer to the team. Traditional team members have experience working in groups and delegating coordination and decision making to colleagues depending on the particular situation. They are trained to be team players and are comfortable working in groups. Most engineers who enter the field of rehabilitation engineering come with a traditional engineering background. Although well versed in design and engineering principles, they often do not receive formal training in group dynamics and need to learn these skills if they are to function effectively. As well, engineers are trained to solve problems with technical solutions. The psychosocial aspects of assisting people with disabilities to make informed choices must be learned most often outside the traditional education stream. Therefore, for the engineer to be a contributing member of the team, not only must he or she bring engineering expertise, but it must be integrated in such a manner that it supports the overall objectives of the technology delivery process, which is to respond to the needs and desires of the consumer.

People with disabilities want to have control over the process and be informed enough to make good decisions. This is quite different from the traditional medical or rehabilitation model, in which well – meaning professionals often tell the individual what is best for him or her. Within this new paradigm, the role is to inform, advise, and educate, not to decide. The professional provides information as to the technical options, prices, etc. and then assists the person who will use the technology to acquire it and learn how to use it.

The Evaluation

An evaluation is meant to guide decision-making for the person with a disability toward appropriate and cost-effective technology. Often, more than one functional need exists for which assistive technology could be prescribed. Costly, frustrating, and time-consuming mistakes often can be avoided if a thorough evaluation based on a person’s total functional needs is performed before any technology is recommended. Following the evaluation, a long-range plan for acquisition and training in the chosen technology can be started.

For example, suppose a person needs a seating system, both a powered and manual wheelchair, an augmentative communication device, a computer workstation, and an environmental control unit (ECU). Where does one begin? Once a person’s goals and priorities have been established, the process can begin. First, a decision would likely be made about the seating system that will provide appropriate support in the selected manual chair. However, the specifications of seating system should be such that the components also can be interfaced into the powered chair. The controls for the computer and augmentative commu­nication device must be located so that they do not interfere with the display of the communication device and must in some way be compatible wit the controls for the ECU. Only if all functional needs are addressed can the technology be acquired in a logical sequence and in such a manner that all components will be compatible. The more severely disabled the individual, the more technology he or she will need, and the more essential is the process of setting priorities and ensuring compatibility of technical components.

In summary, as suggested by the conceptual model, the process begins by gaining an understanding of the person’s sensory, cognitive, and functional abilities, combined with clarification of his or her desires and needs. These are then filtered through the technology options, both in terms of how the interface will integrate with the abilities of the user and how the technology itself will be integrated to meet the defined needs. This information and the associated pros and cons are conveyed to the user, or in some cases their caregiver, who then has the means to participate in the ultimate selection decisions.

Service Delivery Models

People with disabilities can access technology through a variety of different service delivery models. A team of professionals might be available in a university setting where faculty not only teach but also deliver technical services to the community. More traditionally, the team of rehabilitation professionals, Including a rehabilitation engineer, might be available at a hospital or rehabilitation facility. More recently, technology professionals might be in private practice either individually, as a team, or part of the university, hospital, or rehabilitation facility structure. Another option is the growing number of reha­bilitation technology suppliers (RTSs) who offer commercial technology services within the community. They work in conjunction with an evaluation specialist and advise consumers as to the technical options available to meet their needs. They then sell and service the technology and train the consumer in its use. Local chapters of national disability organizations such as United Cerebral Palsy and Easter Seals also may have assistive technology services. In recent years, a growing number of centers for independent living (CILs) have been developed in each state with federal support. Some of these centers have opted to provide assistive technology services, in addition to their information and referral services, which are common to all CILs. And finally, there are volunteers, either in engineering schools or community colleges (student supervised projects) or in industry (high-technology industries often have staff interested in doing community service), such as the Telephone Pioneers. Each model has its pros and cons for the consumer, and only after thoroughly researching the options will the person needing the service make the best choice as to where to go with his or her need in the community. A word of caution. Only if there is timely provision and follow-up available is a service delivery system considered appropriate, even if the cost of the service is less.

A more extensive description of service delivery options may be reviewed in a report that resulted from a RESNA-organized conference on service delivery [ANSI/RESNA, 1987].

Education and Quality Assurance

Professionals on the assistive technology team have a primary degree and credential in their individual professions. For example, the occupational or physical therapist will have a degree and most often state licensure in occupational or physical therapy. The engineer will have recognized degrees in mechanical, electrical, biomedical, or some other school of engineering. However, in order to practice effectively in the field of assistive technology, almost all will need advanced training. A number of occupational therapy curriculums provide training in assistive technology, but not all. The same is true of several of the others. Consumers and payers of assistive technology need to know that professionals practicing in the field of assistive technology have a certain level of competency. For this reason, all professionals, including reha­bilitation engineers, are pursuing the ATP (assistive technology practitioner) credential through RESNA.


RESNA, an interdisciplinary association of persons dedicated to the advancement of assistive technology for people with disabilities, has a credentialing program that credentials individuals on the assistive technology team. As part of the process, the minimum skills and knowledge base for practitioners is tested. Ties with professional organizations are being sought so that preservice programs will include at least some of the knowledge and skills base necessary. Continuing education efforts by RESNA and others also will assist in building the level of expertise of practitioners and consumers. At this time RESNA has a voluntary credentialling process to determine if a person meets a predetermined minimal standard of practice in the field of Assistive Technology. Persons who meet the prerequisite requirements, pass a written exam, and agree to abide by the RESNA Standards of Practice can declare themselves as RESNA certified. They can add the ATP if they are practitioners or ATS if they are suppliers of assistive technology.

Payment for technology and the services required for its application is complex and changing rapidly as health care reform evolves. It is beyond the scope of this discussion to detail the very convoluted and individual process required to ensure that people with disabilities receive what they need. However, there are some basic concepts to be kept in mind. Professionals need to be competent. The documentation of need and the justification of selection must be comprehensive. Time for a person to do this must be allocated if there is to be success. Persistence, creativity, education of the payers, and documentation of need and outcomes are the key issues.

Specific Impairments and Related Technologies

Current information related to specific technologies is best found in brochures, trade magazines (Report Rehab), exhibit halls of technology-related conferences, and databases such as ABLEDATA. Many suppliers and manufacturers are now maintaining Websites, which provides a quick means to locate information on current products. What follows is only a brief introduction to specific disabilities areas to which assistive technology applications are commonly used.


Mobility technologies include wheelchairs, walkers, canes, orthotic devices, FES (functional electrical stimulation), laser canes, and any other assistive device that would assist a person with a mobility impairment, be it motor or sensory, to move about in his or her environment. There are very few people who have a working knowledge of all the possible commercial options. Therefore, people usually acquire expertise in certain areas, such as wheelchairs. There are hundreds of varieties of wheelchairs, each offering a different array of characteristics that need to be understood as part of the selection process. Fortunately, there are now several published ways that the practitioner and the consumer can obtain useful informa­tion. A classification system has been developed that sets a conceptual framework for understanding the different types of wheelchairs that are produced commercially [Hobson, 1990]. Paraplegic News and Sports and Spokes annually publish the specifications on most of the manual and powered wheelchairs commonly found in the North American marketplace. These reviews are based on standardized testing that is carried out by manufacturers following the ANSI/RESNA wheelchair standards [ANSI/RESNA, 1990]. Since the testing and measurements of wheelchairs are now done and reported in a standard way, it is possible to make accurate comparisons between products, a tremendous recent advancement for the wheelchair specialist and the users they serve [Axelson et al., 1994].

Possibly the most significant advancement in wheelchairs is the development and application of industry, on an international scale, for testing the safety and durability of their products. These standards also mandate what and how the test information should be made available in the manufacturer’s presale literature. The Rehabilitation Engineering Research Center and University of Pittsburgh [RERC, 1999] maintains a large Website, where among its many resources is a listing of wheelchair research publications and a general reference site, termed Wheelchairnet [Wheelchairnet, 1999]. The RERC site also tracks the current activities occurring in many of the wheelchair standards working groups. Finally, Cooper [1995, 1998] has published two excellent reference texts on rehabilitation engineering with emphasis on wheeled mobility.


Many people cannot use the wheelchairs as they come from the manufacturer. Specialized seating is required to help persons to remain in a comfortable and functional seated posture for activities that enable them to access work and attend educational and recreational activities. Orthotic supports, seating systems in wheelchairs, chairs that promote dynamic posture in the workplace, and chairs for the elderly that fit properly, are safe, and encourage movement all fit into the broad category of sitting technology.


People with no sensation are prone to skin injury. Special seating technology can assist in the preventions of tissue breakdown. Specially designed cushions and backs for wheelchairs and mattresses that have pressure-distributing characteristics fall into this category. Technology also has been developed to measure the interface pressure. These tools are now used routinely to measure and record an individual’s pressure profile, making cushion selection and problem solving more of a science than an art.

Again, a classification system of specialized seating has been developed that provides a conceptual framework for understanding the features of the various technologies and their potential applications. The same reference also discusses the selection process, evaluation tools, biomechanics of supported sitting, and materials properties of weight-relieving materials [Hobson, 1990].

Access (Person-Machine Interface)

In order to use assistive technology, people with disabilities need to be able to operate the technology. With limitations in motor and/or sensory systems, often a specially designed or configured interface system must be assembled. It could be as simple as several switches or a miniaturized keyboard or as complex as an integrated control system that allows a person to drive a wheelchair and operate a computer and a communication device using only one switch.


Because of motor or sensory limitations, some individuals cannot communicate with spoken or written word. There are communication systems that enable people to communicate using synthesized voice or printed output. Systems for people who are deaf allow them to communicate over the phone or through computer interfaces. Laptop computers with appropriate software can enable persons to communicate faster and with less effort than previously possible. Some basic guidelines for selecting an augmentative communication system, including strategies for securing funding, have been proposed in an overview chapter by James Jones and Winifred Jones [Jones & Jones, 1990].


Modified vans and cars enable persons with disabilities to independently drive a vehicle. Wheelchair tie­downs and occupant restraints in personal vehicles and in public transportation vehicles are allowing people to be safely transported to their chosen destination. Fortunately, voluntary performance standards for restraint and tie-down technologies have been developed by a task group within the Society for Automotive Engineers (SAE). Standards for car hand controls, van body modifications, and wheelchair lifts are also available from SAE. These standards provide the rehabilitation engineer with a set of tools that can be used to confirm safety compliance of modified transportation equipment. Currently in process and still requiring several more years of work are transport wheelchair and vehicle power control standards.

Activities of Daily Living (ADL)

ADL technology enables a person to live independently as much as possible. Such devices as environ­mental control units, bathroom aids, dressing assists, automatic door openers, and alarms are all con­sidered aids to daily living. Many are inexpensive and can be purchased through careful selection in stores or through catalogues. Others are quite expensive and must be ordered through vendors who specialize in technology for independent living.

Ron Mace, now deceased and creator of the Center for Universal Design at the North Carolina State University, is widely acknowledged as the father of the Universal Design concept. The concept of universal design simply means that if our everyday built environments and their contained products could be designed to meet the needs of a wider range of people, both young and old, then the needs of more persons with disabilities would be met without the need for special adaptions [Center for Universal Design, 1999]. Others like Paul Grayson have also published extensively regarding the need to re-think how we design our living environments [Grayson, 1991]. Vanderheiden and Denno have prepared human factors guidelines that provide design information to allow improved access by the elderly and persons with disabilities [Denno et al., 1992; Vanderheiden & Vanderheiden, 1991; Trace Center, 1999].

School and Work

Technology that supports people in the workplace or in an educational environment can include such applications as computer workstations, modified restrooms, and transportation to and from work or school. Students need the ability to take notes and do assignments, and people working have a myriad of special tasks that may need to be analyzed and modified to enable the employee with the disability to be independent and productive. Weisman has presented an extensive overview of rehabilitation engineer­ing in the workplace, which includes a review of different types of workplaces, the process of accommo­dation, and many case examples [Weisman, 1990].


A component of living that is often overlooked by the professional community is the desire and, in fact, need of people with disabilities to participate in recreational activities. Many of the adaptive recreational technologies have been developed by persons with disabilities themselves in their effort to participate and be competitive in sports. Competitive wheelchair racing, archery, skiing, bicycles, and technology that enables people to bowl, play pool, and fly their own airplanes are just a few areas in which equipment has been adapted for specific recreational purposes.

Community and Workplace Access

There is probably no other single legislation that is having a more profound impact on the lives of people with disabilities then the Americans with Disabilities Act (ADA), signed into law by President Bush in August of 1990. This civil rights legislation mandates that all people with disabilities have access to public facilities and that reasonable accommodations must be made by employers to allow persons with dis­abilities to access employment opportunities. The impact of this legislation is now sweeping America and leading to monumental changes in the way people view the rights of persons with disabilities.

Future Developments

The field of rehabilitation engineering, both in research and in service delivery, is at an important crossroad in its young history. Shifting paradigms of services, reduction in research funding, consumer­ism, credentialing, health care reform and limited formal educational options all make speculating on what the future may bring rather hazy. Given all this, it is reasonable to say that one group of rehabilitation engineers will continue to advance the state of the art through research and development, while another group will be on the front lines as members of clinical teams working to ensure that individuals with disabilities receive devices and services that are most appropriate for their particular needs.

The demarcation between researchers and service providers will become clearer, since the latter will become credentialed. RESNA and its professional specialty group (PSG) on rehabilitation engineering are working out the final credentialing steps for the Rehabilitation Engineer RE and the Rehabilitation Engineering Technologist RET. Both must also be an ATP. They will be recognized as valued members of the clinical team by all members of the rehabilitation community, including third-party payers, who will reimburse them for the rehabilitation engineering services that they provide. They will spend as much or more time working in the community as they will in clinical settings. They will work closely with consumer-managed organizations who will be the gatekeepers of increasing amounts of government – mandated service dollars.

If these predictions come to pass, the need for rehabilitation engineering will continue to grow. As medicine and medical technology continue to improve, more people will survive traumatic injury, disease, and premature birth, and many will acquire functional impairments that impede their involvement in personal, community, educational, vocational, and recreational activities. People continue to live longer lives, thereby increasing the likelihood of acquiring one or more disabling conditions during their lifetime. This presents an immense challenge for the field of rehabilitation engineering. As opportunities grow, more engineers will be attracted to the field. More and more rehabilitation engineering education programs will develop that will support the training of qualified engineers, engineers who are looking for exciting challenges and opportunities to help people live more satisfying and productive lives.


ANSI/RESNA. 1990. Wheelchair Standards. RESNA Press, RESNA, 1700 Moore St., Arlington, VA 22209-1903.

Axelson P, Minkel J, Chesney D. 1994. A Guide to Wheelchair Selection: How to Use the ANSI/RESNA Wheelchair Standards to Buy a Wheelchair. Paralyzed Veterans of America (PVA).

Bain BK, Leger D. 1997. Assistive Technology. An Interdisciplinary Approach. Churchill Livingstone, New York.

Center for Universal Design, 1999. Http://www. design. ncsu. edu/cud/

Cook AM, Hussey SM. 1995. Assistive Technologies: Principles and Practice. Mosby, St. Louis, MO.

Cooper RA. 1995. Rehabilitation Engineering Applied to Mobility and Manipulation. Institute of Physics Publishing, Bristol, U. K.

Cooper RA. 1998. Wheelchair Selection and Configuration. Demos Medical Publishing, New York.

Deno JH, et al. 1992. Human Factors Design Guidelines for the Elderly and People with Disabilities. Honeywell, Inc., Minneapolis, MN 55418 (Brian Isle, MN65-2300).

Galvin JC, Scherer MJ. 1996. Evaluating, Selecting, and Using Appropriate Assistive Technology, Aspen Publishers, Gaithersburg, MD.

Hobson DA. 1990. Seating and mobility for the severely disabled. In R Smith, J Leslie (eds), Rehabilitation Engineering, pp 193-252. CRC Press, Boca Raton, FL.

Jones D, Jones W. 1990. Criteria for selection of an augmentative communication system. In R Smith, J Leslie (eds), Rehabilitation Engineering, pp 181-189. CRC Press, Boca Raton, FL.

Medhat M, Hobson D. 1992. Standardization of Terminology and Descriptive Methods for Specialized Seating. RESNA Press, RESNA, 1700 Moore St., Arlington, VA 22209-1903.

Rehabilitation Technology Service Delivery—A. Practical Guide. 1987. RESNA Press, RESNA, 1700 Moore St., Arlington, VA 22209-1903.

Smith RO. 1992. Technology and disability. AJOT 1(3):22.

Society for Automotive Engineers. 1994. Wheelchair Tie-Down and Occupant Restraint Standard (com­mittee draft). SAE. Warrendale, PA.

Trace Center, 1999. Http://trace. wisc. edu/

Vanderheiden G, Vanderheiden K. 19991. Accessibility Design Guidelines for the Design of Consumer Products to Increase their Accessibility to People with Disabilities or Who Are Aging. Trace R&D Center, University of Wisconsin, Madison, WI.

Weisman G. 1990. Rehabilitation engineering in the workplace. In R Smith, J Leslie (eds), Rehabilitation Engineering, pp 253-297. CRC Press, Boca Raton, FL.

WheelchairNet, 1999. Http://www. wheelchairnet. org

Further Information

ABLEDATA, 8455 Colesville Rd., Suite 935, Silver Spring, Md. 20910-3319.

Geddes, L. G. “Historical Perspectives 4 – Electromyography ” The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000

Historical Perspectives 4


Leslie A. Geddes Early Investigations

Purdue University Clinical Electromyography

Early Investigations

The study of bioelectricity started with the Galvani-Volta controversy over the presence of electricity in frog muscle [see Geddes and Hoff, 1971]. Galvani likened the sciatic nerve-gastrocnemius muscle to a charged Leyden jar (capacitor) in which the nerve was the inner conductor and the surface of the muscle was the outer conductor. Therefore, Galvani thought that by joining the two with an arc of dissimilar metals, the biologic capacitor was discharged and the muscle twitched. Volta proved conclusively that it was the dissimilar metals in contact with tissue fluid that was the stimulus.

Interestingly, it was found that when the sciatic nerve of a nerve-muscle preparation was laid on the cut end of another frog muscle and the nerve was touched to the intact surface, the muscle of the nerve – muscle preparation twitched. Here was evidence of stimulation without metal conductors; this experi­ment was performed by Matteucci [1842].

With the first galvanometers, it was shown that current would be indicated when one electrode was placed on the cut end of a frog muscle and the other on the intact surface. This phenomenon became known as the injury current or frog current, the cut surface being negative to the intact surface.

Whereas the foregoing experiments showed that skeletal muscle possessed electricity, little was known about its relation to contraction. Matteucci [1842] conducted an ingenious experiment in which he placed the nerve of a second nerve-muscle preparation on the intact muscle of a first such preparation and stimulated the nerve of the first using an inductorium. He discovered that both muscles contracted. Here is the first evidence of the electric activity of contracting skeletal muscle.

Matteucci and DuBois-Reymond both found that the injury current disappeared when a muscle was contracted tetanically. This observation led directly to the concept of a resting membrane potential and its disappearance with activity [see Hoff and Geddes, 1957].

That human muscle, as well as frog muscle, produced electric activity was demonstrated by Du Bois – Reymond [1858] in the manner shown in Fig. HP4.1. With electrodes in saline cups connected to a galvanometer, Du Bois-Reymond stated that as soon as the fingers were placed in the cups, the galva­nometer needle deflected, and it required some time for a position of equilibrium to be attained. Du Bois-Reymond [1858] stated:

As soon as this state [equilibrium] is attained, the whole of the muscles of one of the arms must be so braced that an equilibrium may be established between the flexors and the extensors of all the articulations of the limb, pretty much as in a gymnastic school is usually done when one wants to let a person feel the development of one’s muscles.

As soon as this is done, the [galvanometer] needle is thrown into movement, its deflection being uniformly in such a sense as to indicate in the braced arm "an inverse current," according to Nobili’s nomenclature; that is to say, a current passing from the hand to the shoulder. The braced arm then

Rehabilitation Engineering Technologies: Principles of Application

FIGURE HP4.1 The first evidence contracting skeletal muscle in man produces an electrical signal. (From Du Bois – Reymond [1858].)

Acts the part of the copper in the compound arc of zinc and copper mentioned above. [Du Bois – Reymond was referring to the polarity of a voltaic cell in which zinc and copper are the positive and negative electrodes, respectively.]

The rheotome and slow-speed galvanometer were used to reconstruct the form of the muscle action potential. However, it was desired to know the time course of the electric change associated with a single muscle contraction (twitch), as well as its relationship to the electrical event (action potential). A second item of interest was the so-called latent period, that time between the stimulus and the onset of muscle contraction, which Helmholtz [1853] reported to be 10 ms for frog muscle.

Waller [1887] set himself the task of measuring the latent period and the relationship between the action potential and the force developed by frog gastrocnemius muscle in response to a single stimulus. He found that the onset of the twitch was later than the onset of the action potential, as shown in Fig. HP4.2. However, the true form of the muscle action potential and its relationship to the onset of the twitch had to await the development of the micropipet electrode, the vacuum-tube amplifier, and the cathode-ray oscilloscope. In 1957, Hodgkin and Horowicz [1957] recorded the twitch and action potential of a single muscle fiber of the frog. Figure HP4.3 Is a copy of their record. Note that the onset of the action potential precedes the onset of muscle contraction by about 4 ms. We know that it is the action potential that triggers the release of mechanical energy.

Rehabilitation Engineering Technologies: Principles of Application

FIGURE HP4.2 Relationship between the twitch (recorded with a myograph, m) and the action potential (recorded with a capillary electrometer, e) of a frog gastrocnemius muscle. The time marks (t) are 1/20 s. (From Waller [1887].)








0 10 20 30 msec

FIGURE HP4.3 The relationship between the muscle action potential and the force of concentration in a single skeletal muscle fiber in the frog. (From Hodgkin and Horowicz [1957].)

Clinical Electromyography

It was well known that when a nerve that innervates a skeletal muscle is cut, the muscle is paralyzed immediately; however, days to weeks later (depending on the species), on careful visual examination, the individual muscle fibers are seen to be contracting and relaxing randomly, i. e., fibrillating. The first to bring the facts together regarding normal muscle action potentials and denervation-fibrillation potentials were Denny-Brown and Pennybacker in the United Kingdom [1939]; the date and locale are highly significant. They distinguished between involuntary twitching of innervated muscle and fibrillation of denervated muscle by recording both the electric and mechanical activity of muscles. Two instrumental advances made their study possible: (1) the use of a hypodermic needle electrode inserted into the muscle and (2) the use of a rapidly responding, mirror-type photographic recorder, the Matthews [1928] oscillograph.

The cathode-ray tube was not generally available in the United Kingdom when Matthews [1928] constructed his moving-tongue mirror oscillograph. The device consisted of a piece of soft iron mounted on a steel leaf spring, as shown in Fig. HP4.4. A strong electromagnet attracted the soft iron, which bent

Rehabilitation Engineering Technologies: Principles of Application

FIGURE HP4.4 The Matthews moving-tongue oscillograph and amplifier used with it. The electromagnetic coil (A) provided an attractive force on the tongue (soft iron and steel spring); the signal current applied to the small coils aided or opposed this force and caused the tongue to bend more or less and hence move the mirror which reflected a beam of light on to a recording surface. (From BHC Matthews, 1928, J. Physiol (Lond) 65:225, with permission.)

The steel (leaf-spring) support. Two coils mounted on the pole faces were connected to the output tubes of a five-stage, single-sided, resistance-capacitance coupled amplifier. The amplified potentials altered the current in the electromagnet coils, causing more or less bending of the leaf spring, thereby tilting the mirror mounted on the leaf spring and permitting photographic recording of action potentials.

With the Matthews oscillograph, Denny-Brown and Pennybacker [1939] laid the foundation for clinical electromyography when they reported as follows:

Denervated muscle fibers contract periodically, and the confused medley of small twitches constitutes true fibrillation. The movement is so slight that it can seldom be seen in the clinic. The twitchings appear to be due to heightened excitability of the sarcolemma or rapidly conducting portion of the muscle fibre to traces of free acetylcholine in the tissues.

Reinnervated muscle is free from such involuntary excitation, except for the curious "contracture" of facial muscle, which consists of periodic, intense repetitive discharges which suggest a central mechanism.

Earlier it was stated that 1939 was significant; this was the year when World War II broke out in Europe. Soon motor nerve injuries due to shrapnel wounds began to appear in large numbers, and the need for electromyography to identify denervation fibrillation potentials and their gradual disappearance with reinnervation was urgent. The first electromyograph in North America was developed by Herbert Jasper at McGill University (Montreal Neurological Institute). Starting in 1942, design concepts were developed, and by 1944 prototypes had been built and used clinically. In his report to the Committee on Army Medical Research, Capt. Jasper [1945] stated:

The present equipment has been developed over a period of about 18 months experimentation with different designs of electromyograph for use on hospital wards. Numerous modifications of design have been incorporated in the present model in order to provide simplicity of operation, portability, freedom from electrical interference, and perfection of both the audible and visible analysis of the electrical activity of normal and diseased muscles. A portable clinical electromyograph has been developed which has proven to be practical in operation on hospital wards to aid in the diagnosis and prognosis of muscles paralyzed by traumatic injuries of their nerve supply. Four complete units have been constructed for use in special centers for the treatment of nerve injuries.

The Royal Canadian Army Medical Corps (RCAMC) electromyograph had many unique design features that were incorporated in all later commercially available electromyographs. It consisted of three units, a small battery-operated three-stage differential amplifier (Fig. HP4.5) And an oscilloscope (Fig. HP4.6), Both placed on a loudspeaker cabinet on rubber-wheel casters (Fig. HP4.7). Thus simulta­neous visual display and aural monitoring of normal motor units and fibrillation potentials was possible.

The preamplifier (Fig. HP4.5) Was very carefully constructed, the input tubes being supported by rubber-mounted antimicrophonic sockets. The grid resistors (R9) and plate resistors (R8) were wire wound and carefully matched. A high common-mode rejection ratio was obtained by matching the input tubes and adjustment of the potentiometer (R7) in the screen-grid supply. A common-mode rejection ratio in excess of 10,000 was easily achieved. The overall frequency response extended from 3 to 10,000 Hz.

The cathode-ray oscilloscope (Fig. HP4.6) Was of unusual design for that time because the sweep velocity was independent of the number of sweeps per second, a feature to appear much later in oscilloscopes. A linear sweep (time base) was obtained by charging a capacitor (0.1 OF) through a pentode (6U7G) which acted as a constant-current device. The sweep was initiated at a rate of about 7 times per second by the multivibrator (6N7), which also provided an output to enable stimulating a nerve, the stimulus occurring at the beginning of each sweep, thereby permitting nerve conduction-time measure­ments. The oscilloscope also contained the audio amplifier (6L6). The cathode-ray tube had a short – persistence, blue-white phosphor that produced brilliant blue-white images of remarkable clarity. A camera was used to obtain photographic records of the waveforms, which were optimized by listening to them via the loudspeaker (as advocated by Adrian) as the needle electrode was being inserted and adjusted. Fig. HP4.8 Illustrates typical action potentials.

At the end of the war (1945), oscilloscopes became available, and Fig. HP4.7 Shows the RCAMC electromyograph with a Cossor oscilloscope (right) and the high-gain differential amplifier (left), both on the loudspeaker cabinet, which was on casters. The recessed opening at the top of the loudspeaker cabinet face provided access to the on-off and volume controls.

In addition to the creation of a high-performance EMG unit, Jasper introduced the monopolar needle electrode system used in all subsequent EMGs. The needle electrode was insulated with varnish down to its tip and was paired with a skin-surface electrode of silver. The patient was grounded by another electrode taped to the same member that was being examined. Figure HP4.8 illustrates application of the electrodes and typical motor-unit and fibrillation potentials. The report by Jasper and Ballem [1949] summarized the experience with the RCAMC electromyograph and laid the foundation for diagnostic EMG.

World War II ended in 1945, after which electromyographs became available commercially. Their features were essentially the same as those embodied in the RCAMC electromyograph. From the begin­ning, these units were completely power-line-operated, the author’s master’s thesis describing the first of these units.

Rehabilitation Engineering Technologies: Principles of Application

FIGURE HP4.5 The three-stage resistance-capacity coupled differential amplifier used is the RCAMC electromyo­graphy. (From Jasper et al. [1945].)

Rehabilitation Engineering Technologies: Principles of Application

FIGURE HP4.6 The oscilloscope, loudspeaker amplifier, and stimulator unit of the RCAMC electromyograph. (From Jasper et al. [1945].)

Rehabilitation Engineering Technologies: Principles of Application

Rehabilitation Engineering Technologies: Principles of Application

FIGURE HP4.8 Electrode arrangement and action potentials with the RCAMC electromyograph. (Redrawn from Jasper and Ballem [1949].)


Denny-Brown D, Pennybacker JB. 1939. Fibrillation and fasciculation in voluntary muscle. Brain 61:311.

Du Bois-Reymond E. 1858. Untersuchungen uber thierische Elekticitat. Moleschott’s Untersuch. Z Natur Mensch 4:1.

Geddes LA, Hoff HE. 1971. The discovery of bioelectricity and current electricity (the Galvani-Volta controversy). IEEE Spect 8(12):38.

Helmholtz H. 1853. On the methods of measuring very small portions of time and their application to physiological purposes. Philos Mag J Sci 6:313.

Hodgkin AL, Horowicz P. 1957. The differential action of hypertonic solutions on the twitch and action potential of a muscle fiber. J Physiol (Lond) 136:17P.

Hoff HE, Geddes LA. 1957. The rheotome and its prehistory: A study in the historical interrelation of electrophysiology and electromechanics. Bull Hist Med 31(3):212.

Jasper HH, Ballem G. 1949. Unipolar electromyograms of normal and denervated human muscle. J Neurophysiol 12:231.

Jasper HH. 1945. The RCAMC electromyograph, Mark II. With the technical assistance of Lt. RH Johnston and LA Geddes. Report submitted to the Associate Committee on Army Medical Research, National Research Council of Canada, 27 April 1945.

Matteucci C. 1842. Duxieme memoire sur le courant electrique propre de la grenouille et sur celui des animaux a sang chaud. Ann Chim Phys 3S(6):301.

Matthews BHC. 1928. A new electrical recording system for physiological work. J Physiol (Lond) 65:225.

Waller AD. 1887. A demonstration on man of electromotive changes accompanying the heart’s beat. J Physiol (Lond) 8:229.

Kondraske, G. V. “Human Performance Engineering.” The Biomedical Engineering Handbook: Second Edition. Ed. Joseph D. Bronzino Boca Raton: CRC Press LLC, 2000





George V. Kondraske

University of Texas at Arlington,

Human Performance Institute

A Working Model for Human System-Task Interfaces George V Kondraske Background • Basic Principles • Application Issues • Conclusion

Measurement of Neuromuscular Performance Capacities Susan S. Smith Neuromuscular Functional Units • Range of Motion and Extremes of Motion • Strength • Speed of Movement • Endurance • Reliability, Validity, and Limitations in Testing • Performance Capacity Space Representations • Conclusions

Measurement of Sensory-Motor Control Performance Capacities:

Tracking Tasks Richard D. Jones

Basic Principles • Measurement of Sensory-Motor Control Performance • Analysis of Sensory-Motor Control Performance

Measurement of Information-Processing Performance Capacities

George V. Kondraske, Paul J. Vasta

Basic Principles • General Measurement Paradigms • Measurement Instruments and Procedures • Present Limitations

High-Level Task Analysis: Mental Components Kenneth J. Maxwell Fundamentals • Mental Task Analysis: Process and Methods • Models of Human Mental Processing and Performance • Models of Machine Processing Capabilities • Human – Machine Task Analytic Framework • Brief Example: Analysis of Supervisory Control Task

Task Analysis and Decomposition: Physical Components Sheik N. Imrhan Fundamental Principles • Early Task-Analysis Methods • Methods of Physical Task Analysis • Factors Influencing the Conduct of Task Analysis • Measurement of Task Variables • Uses and Applications of Task Analysis • Future Developments

Human-Computer Interface Design Issues Kenneth J. Maxwell Fundamentals • A Quantitative Performance-Based Model of Usability • Selected HCI Design Issues, Goals, and Resource Requirements

Applications of Human Performance Measurements to Clinical Trials to

Determine Therapy Effectiveness and Safety Pamela J. Hoyes Beehler,

Karl Syndulko

Basic Principles: Types of Studies • Methods • Representative Application Examples •

Future Needs and Anticipated Developments

Applications of Quantitative Assessment of Human Performance in

Occupational Medicine Mohamad Parnianpour

Principles • Low Back Pain and Trunk Performance • Clinical Applications • Conclusions

Human Performance Engineering: Computer-Based Design and Analysis Tools

Paul J. Vasta, George V. Kondraske

Selected Fundamentals • Scope, Functionality, and Performance • Functional Overview of

Representative Packages • Anticipated Development

Human Performance Engineering: Challenges and Prospects for the Future

George V. Kondraske

Models • Measurements • Databases and Data Modeling • Summary


HE ULTIMATE GOAL OF HUMAN performance engineering is enhancement of the performance and safety of humans in the execution of tasks. The field (in a more formalized sense) was fueled initially by military applications but has become an important component in industrial settings as well. In a biomedical engineering context, the scope of definition applied to the term human not only encompasses individuals with capabilities that differ from those of a typical healthy individual in many possible different ways (e. g., individuals who are disabled, injured, unusually endowed, etc.) but also includes those who are “healthy” (e. g., health care professionals). Consequently, one finds a wide range of problems in which human performance engineering and associated methods are employed. Some examples include

Evaluation of an individual’s performance capacities for determining the efficacy of new thera­peutic interventions or so-called level of disability for worker’s compensation and other medical – legal purposes.

Design of assistive devices and/or work sites in such a way that a person with some deficiency in his or her “performance resource profile” will be able to accomplish specified goals.

Design of operator interfaces for medical instruments that promote efficient, safe, and error-free use.

In basic terms, each of these situations involves one or more of the following: (1) a human, (2) a task or tasks, and (3) the interface of a human and task(s). Human performance engineering emphasizes concepts, methods, and tools that strive toward treatment of each of these areas with the engineering rigor that is routinely applied to artificial systems (e. g., mechanical, electronic, etc.). Importance is thus placed on models (a combination of cause-and-effect and statistical), measurements (of varying degrees of sophistication that are selected to fit needs of a particular circumstance), and various types of analyses. Whereas many specialty areas within biomedical engineering begin with an emphasis on a specific subsystem and then proceed to deal with it at lower levels of detail (sometimes even at the molecular level) to determine how it functions and often why it malfunctions, human performance engineering emphasizes subsystems and their performance capacities (i. e., how well a system functions), the integra­tion of these into a whole and their interactions, and their operation in the execution of tasks that are of ultimate concern to humans. These include tasks of daily living, work, and recreation. In recent years, there has been an increased concern within medical communities on issues such as quality of life, treatment outcome measures, and treatment cost-effectiveness. By linking human subsystems into the “whole” and discovering objective quantitative relationships between the human and tasks, human performance engineering can play an important role in addressing these and other related concerns.

Human performance engineering combines knowledge, concepts, and methods from across many disciplines (e. g., biomechanics, neuroscience, psychology, physiology, and many others) which, in their overlapping aspect, all deal with similar problems. Among current difficulties is that these wide-ranging efforts are not linked by a conceptual framework that is commonly employed across contributing disci­plines. In fact, few candidate frameworks exist even within the relevant disciplines. One attempt to provide some unification and commonality is presented in Chapter 147 as basis for readers to integrate material in subsequent chapters and from other sources. In a further attempt to enhance continuity across this section, chapter authors have been requested to consider this perspective and to incorporate basic concepts and terms where applicable.

Chapters 148 through 150 look “toward the human” and focus on measurement of human performance capacities and related issues. Owing to a combination of the complexity of the human system (even when viewed as a collection of rather high-level subsystems) and limited space available, treatment is not comprehensive. For example, measurement of sensory performance capacities (e. g., tactile, visual, audi­tory) is not included in this edition. Both systems and tasks can be viewed at various hierarchical levels. Chapters 148 and 149 focus on a rather “low” systems level and discuss basic functional units such as actuator, processor, and memory systems. Chapter 150 moves to a more intermediate level, where speech, postural control gait, and hand-eye coordination systems could be considered. Measurement of structural parameters, which play important roles in many analyses, also is not allocated the separate chapter it deserves (as a minimum) due to space limitations. Chapter 151 and 152 then shift focus to consider the analysis of different types of tasks in a similar, representative fashion.

Chapters 153 through 155 are included to provide insight into a representative selection of application types. Space constraints, the complexity of human performance, and the great variety of tasks that can be considered limit the level of detail with which such material can reasonably be presented. Work in all application areas will begin to benefit from emerging computer-based tools, which is the theme of chapter 156. The section concludes with a look to the future (Chapter 157) that summarizes selected current limitations, identifies some specific research and development needs, and speculates regarding the nature of some anticipated developments.

Many have contributed their talents to this exciting field in terms of both research and applications, yet much remains to be done. I am indebted to the authors not only for their contributions and cooperation during the preparation of this section but also for their willingness to accept the burdens of communicating complex subject matter reasonably, selectively, and as accurately as possible within the imposed constraints.

Kondraske, G. V. “A Working Model for Human System-Task Interfaces.” The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000

Measurement Tools and Processes in Rehabilitation Engineering

In every engineering discipline, measurement facilitates the use of structured procedures and decision­making processes. In rehabilitation engineering, the presence of "a human," the only or major component of the system of interest, has presented a number of unique challenges with regard to measurement. This is especially true with regard to the routine processes of rehabilitation that either do or could incorporate and rely on measurements. This, in part, is due to the complexity of the human system’s architecture, the variety of ways in which it can be adversely affected by disease or injury, and the versatility in the way it can be used to accomplish various tasks of interest to an individual.

Measurement supports a wide variety of assistive device design and prescription activities undertaken within rehabilitation engineering (e. g., Leslie and Smith [1990], Webster et al. [1985], and other chapters within this section). In addition, rehabilitation engineers contribute to the specification and design of measurement instruments that are used primarily by other service providers (such as physical and occupational therapists). As measurements of human structure, performance, and behavior become more rigorous and instruments used have taken advantage of advanced technology, there is also a growing role for rehabilitation engineers to assist these other medical professionals with the proper application of measurement instruments (e. g., for determining areas that are most deficient in an individual’s perfor­mance profile, objectively documenting progress during rehabilitation, etc.). This is in keeping with the team approach to rehabilitation that has become popular in clinical settings. In short, the role of measurement in rehabilitation engineering is dynamic and growing.

In this chapter, a top-down overview of measurement tools and processes in rehabilitation engineering is presented. Many of the measurement concepts, processes, and devices of relevance are common to applications outside the rehabilitation engineering context. However, the nature of the human population with which rehabilitation engineers must deal is arguably different in that each individual must be assumed to be unique with respect to at least a subset of his or her performance capacities and/or structural parameters; i. e., population reference data cannot be assumed to be generally applicable. While there are some exceptions, population labels frequently used such as "head-injured" or "spinal cord – injured" represent only a gross classification that should not be taken to imply homogeneity with regard to parameters such as range of motion, strength, movement speed, information processing speed, and other performance capacities. This is merely a direct realization that many different ways exist in which the human system can be adversely affected by disease or injury and recognition of the continuum that exists with regard to the degree of any given effect. The result is that in rehabilitation engineering, compared with standard human factors design tasks aimed at the average healthy population, many measurement values must be acquired directly for the specific client.

Measurement in the present context encompasses actions that focus on (1) the human (e. g., structural aspects and performance capacities of subsystems at different hierarchical levels ranging from specific neuromuscular subsystems to the total person and his or her activities in daily living, including work),

Assistive devices (e. g., structural aspects and demands placed on the human), (3) tasks (e. g., distances between critical points, masses of objects involved, etc.), and (4) overall systems (e. g., performance achieved by a human-assistive device-task combination, patterns of electrical signals representing the timing of muscle activity while performing a complex maneuver, behavior of an individual before and after being fitted with a new prosthetic devices, etc). Clearly, an exhaustive treatment is beyond the scope of this chapter. Measurements are embedded in every specialized subarea of rehabilitation engineering. However, there are also special roles served by measurement in a broader and more generic sense, as well as principles that are common across the many special applications. Emphasis here is placed on these.

There is no lack of other literature regarding the types of measurement outlined to be of interest here and their use. However, it is diffusely distributed, and gaps exist with regard to how such tools can be integrated to accomplish goals beyond simply the acquisition of numeric data for a given parameter. With rapidly changing developments over the last decade, there is currently no comprehensive source that describes the majority of instruments available, their latest implementations, procedures for their use, evaluation of effectiveness, etc. While topics other than measurement are discussed, Leslie and Smith [1990] produced what is perhaps the single most directly applicable source with respect to rehabilitation engineering specifically, although it too is not comprehensive with regard to measurement, nor does it attempt to be.

Fundamental Principles

Naturally, the fundamental principles of human physiology manifest themselves in the respective sensory, neuromuscular, information-processing, and life-sustaining systems and impact approaches to measure­ment. In addition, psychological considerations are vital. Familiarization with this material is essential to measurement in rehabilitation; however, treatment here is far beyond the scope of this chapter. The numerous reference works available may be most readily found by consulting relevant chapters in this Handbook and the works that they reference. In this section, key principles that are more specific to measurement and of general applicability are presented.

Structure, Function, Performance, and Behavior

It is necessary to distinguish between structure, function, performance, and behavior and measurements thereof for both human and artificial systems. In addition, hierarchical systems concepts are necessary both to help organize the complexity of the systems involved and to help understand the various needs that exist.

Structural measures include dimensions, masses (of objects, limb segments), moments of inertia, circumferences, contours, compliances, and any other aspects of the physical system. These may be considered hierarchically as being pertinent to the total human (e. g., height, weight, etc.), specific body segments (e. g., forearm, thigh, etc.), or components of basic systems such as tendons, ligaments and muscles.

Function is the purpose of the system of interest (e. g., to move a limb segment, to communicate, to feed and care for oneself). Within the human, there are many single-purpose systems (e. g., those that function to move specific limb segments, process specific types of information, etc.). As one proceeds to higher levels, such as the total human, systems that are increasingly more multifunctional emerge. These can be recognized as higher-level configurations of more basic systems that operate to feed oneself, to conduct personal hygiene, to carry out task of a job, etc. This multilevel view of just functions begins to help place into perspective the scope over which measurement can be applied.

In rehabilitation in general, a good deal of what constitutes measurement involves the application of structured subjective observation techniques (see also the next subsection) in the form of a wide range of rating scales [e. g., Fuhrer, 1987; Granger & Greshorn, 1984; Potvin et al., 1985]. These are often termed functional assessment scales and are typically aimed at obtaining a global index of an individual’s ability to function independently in the world. The global index is typically based on a number of items within a given scale, each of which addresses selected, relatively high-level functions (e. g., personal hygiene, mobility, etc.). The focus of measurement for a given item is often in estimate of the level of independence or dependence that the subject exhibits or needs to carry out the respective function. In addition, inventories of functions that an individual is able or not able to carry out (with and without assistance) are often included. The large number of such scales that have been proposed and debated is a consequence of the many possible functions and combinations thereof that exist on which to base a given scale. Functional assessment scales are relatively quick and inexpensive to administer and have a demonstrated role in rehabilitation. However, the nature and levels of measurements obtained are not sufficient for many rehabilitation engineering purposes. This latter class of applications generally begins with a function at the level and of the type used as a constituent component of functional assessment scales, considers the level of performance at which that function is executed more quantitatively, and incorporates one or more lower levels in the hierarchy (i. e., the human subsystems involved in achieving the specific functions of daily life that are of interest and their capacities for performance).

Where functions can be described and inventoried, performance measures directly characterize how well a physical system of interest executes its intended function. performance is multidimensional (e. g., strength, range, speed, accuracy, steadiness, endurance, etc.). Of special interest are the concepts of performance capacity and performance capacity measurement. Performance capacity represents the limits of a given system’s ability to operate in its corresponding multidimensional performance space. In this chapter, a resource-based model for both human and artificial system performance and measurement of their performance capacities is adopted [e. g., Kondraske, 1990, 1995]. Thus the maximum knee flexor strength available (i. e., the resource availability) under a stated set of conditions represents one unique performance capacity of the knee flexor system. In rehabilitation, the terms impairment, disability, and handicap [World Health Organization, 1980] have been prominently applied and are relevant to the concept of performance. While these terms place an emphasis on what is missing or what a person cannot do and imply not only a measurement but also the incorporation of an assessment or judgment based on one or more observations the resource-based performance perspective focuses on "what is present" or "what is right" (i. e., performance resource availability). From this perspective, an impairment can be determined to exist if a given performance capacity is found to be less than a specified level (e. g., less than 5th percentile value of a health reference population). A disability exists when performance resource insufficiency exists in a specified task.

While performance relates more to what a system can do (i. e., a challenge or maximal stress is implied), behavior measurements are used to characterize what a system does naturally. Thus a given variable such as movement speed can relate to both performance and behavior depending on whether the system (e. g.,

Human subsystem) was maximally challenged to respond "as fast as possible" (performance) or simply observed in the course of operation (behavior). It is also possible to observe a system that it is behaving

At one or more of its performance capacities (e. g., at the maximum speed possible, etc.) (see Table 145.1).

Subjective and Objective Measurement Methods

Subjective measurements are made by humans without the aid of instruments and objective measure­ments result from the use of instruments. However, it should be noted that the mere presence of an instrument does not guarantee complete objectivity. For example, the use of a ruler requires a human judgment in reading the scale and thus contains a subjective element. A length-measurement system with an integral data-acquisition system would be more objective. However, it is likely that that even this system would involve human intervention in its use, e. g., the alignment of the device and making the decision as to exactly what is to be measured with it by selection of reference points. Measures with more objectivity (less subjectivity) are preferred to minimize questions of bias. However, measurements that are intrinsically more objective are frequently more costly and time-consuming to obtain. Well-reasoned tradeoffs must be made to take advantage of the ability of a human (typically a skilled professional) to quickly "measure" many different items subjectively (and often without recording the results but using them internally to arrive at some decision).

It is important to observe that identification of the variable of interest is not influenced by whether it is measured subjectively or objectively. This concept extends to the choice of instrument used for objective measurements. This is an especially important concept in dealing with human performance and behavior, since variables of interest can be much more abstract than simple lengths and widths (e. g., coordination, postural stability, etc.) In fact, many measurement variables in rehabilitation historically have tended to be treated as if they were inextricably coupled with the measurement method, confounding debate regarding what should be measured with what should be used to measure it in a given context.

Measurements and Assessments

The basic representation of a measurement itself in terms of the actual units of measure is often referred to as the raw form. For measures of performance, the term raw score is frequently applied. Generally, some form of assessment (i. e., judgment or interpretation) is typically required. Assessments may be applied to (or, viewed from a different perspective, may require) either a single measure of groups of them. Subjective assessments are frequently made that are based on the practitioner’s familiarity with values for a given parameter in a particular context. However, due to the large number of parameters and the amount of experience that would be required to gain a sufficient level of familiarity, a more formal and objective realization of the process that takes place in subjective assessments is often employed. This process combines the measured value with objectively determined reference values to obtain new metrics, or scores, that facilitate one or more steps in the assessment process.

Measurement Tools and Processes in Rehabilitation Engineering

Percent normal □

подпись: percent normal □


подпись:  (°pgFor aspects of performance, percent normal scores are computed by expressing subject Y’s availability of performance resource k[RAk(Y)] as a fraction of the mean availability of that resource in a specified reference population [RAk(pop)]. Ideally, the reference population is selected to match the characteristics of the individual as closely as possible (e. g., age range, gender, handedness, etc.).


Aside from the benefit of placing all measurements on a common scale, a percent normal representation of a performance capacity score can be loosely interpreted as a probability. Consider grip strength as the

© 2000 by CRC Press LLC

подпись: © 2000 by crc press llc

Hierarchical Level


Total human

Human with artificial systems

Structure Function

Height • Multifunction, reconfigurable system

Weight • High-level functions: tasks of daily life

Postures (working, grooming, recreation, etc.)

Subjective • Functional assessment scales

And Single-number global index

Instrumented Level of indep. estimates



No single-number direct measurement is possible

•Possible models to integrate lower-level measures

Direct measurement (subjective and instrumented) of selected performance attribute for selected functions


Subjective self – and family reports

Instrumented ambulatory

Activity monitors (selected attributes)

See notes under “function”




Instrumented methods

подпись: dimensions
instrumented methods

Complex Body Systems



Lifting, gait

Upper extremity



Multifunction, reconfigurable systems

System-specific functions

Function specific subjective rating scales

Often based on impairment/ disability concepts Relative metrics

Some instrumented performance capacity measures

Known also as “functional capacity” (misnomer)

Subjective and automated (objective) videotape evaluation

Instrumented measures of physical quantities vs. time (e. g., forces, angles, motions)

Electromyography (e. g., muscle timing patterns, coordination)

Basic Systems

Visual information processors

Flexors, extensors

Visual sensors

Auditory sensors



подпись: basic systems
visual information processors
flexors, extensors
visual sensors
auditory sensors




Moments of inertia

Instrumented methods

подпись: dimensions
moments of inertia
instrumented methods

Single function

System-specific functions

Subjective estimates by clinician for diagnostic and routine monitoring purposes

Instrumented measures of performance capacities (e. g., strength, extremes/range of motion, speed, accuracy, endurance, etc.)

Instrumented systems

Measure and log electrophysiologic biomechanical, and other variables vs. time

Post-hoc parameterization

Mechanical properties

Instrumented methods/imaging

подпись: mechanical properties
instrumented methods/imaging
Measurement Tools and Processes in Rehabilitation Engineering

Components of basic systems





Generally single-function

Component-specific functions

Difficult to assess for individual subjects

Infer from measures at “basic system level”

Direct measurement methods with lab samples, research applications

Difficult to assess for individual subject

Direct measurement methods with lab samples, research applications

Note: Structure, function, performance, and behavior are encompassed at multiple hierarchical levels. Both subjective and objective, instrumented methods of measurement are employed.

Performance resource. Assume that there is a uniform distribution of demands of demands placed on grip strength across a representative sample of tasks of daily living, with requirements ranging from zero to the value representing mean grip strength availability in the reference population. Further assuming

That grip strength was the only performance resource that was in question for subject Y (i. e., all other

Were available in nonlimiting amounts), the percent normal score would represent the probability that

A task involving grip strength, randomly selected from those which average individuals in the reference population could execute (i. e., those for which available grip strength would be adequate), could be

Successfully executed by subject Y. While the assumptions stated here are unlikely to be perfectly true, this type of interpretation helps place measurements that are most commonly made in the laboratory into daily-life contexts.

In contrast to percent normal metrics, z-scores take into account variability within the selected reference

Population. Subject Y’s performance is expressed in terms of the difference between it and the reference

Population mean, normalized by a value corresponding to one standard deviation unit (□) of the reference population distribution:

Measurement Tools and Processes in Rehabilitation Engineering

подпись: □(145.2)

It is important to note that valid z-scores assume that the parameter in question exhibit a normal distribution in the reference population. Moreover, z-scores are useful in assessing measures of structure, performance, and behavior. With regard to performance (and assuming that measures are based on a resource construct, i. e., a larger numeric value represents better performance), a z-score of zero is produced when the subject’s performance equals that of the mean performance in the reference popu­lation. Positive z-scores reflect performance that is better than the population mean. In a normal distri­bution, 68.3% of the samples fall between z-scores of -1.0 and +1.0, wile 95.4% of these samples fall between z-scores of -2.0 and +2.0. Due to variability of a given performance capacity within a healthy population (e. g., some individuals are stronger, faster, more mobile that others), a subject with a raw performance capacity score that produces a percent normal score of 70% could easily produce a z-score of -1.0. Whereas this percent normal score might raise concern regarding the variable of interest, the z-score of -1.0 indicates that a good fraction of healthy individuals exhibit lower level of performance


Both percent normal and z-scores require reference population data to compute. The best reference (i. e., most sensitive) is data for that specific individual (e. g., preinjury or predisease onset). In most cases, these data do not exist. However, practices such as preemployment screenings and regular checkups are beginning to provide individualized reference data in some rehabilitation contexts.

In yet another alternative, it is frequently desirable to use values representing demands imposed by tasks [RDk(task A)] as the reference for assessment of performance capacity measures. Demands on performance resources can be envisioned to vary over the time course of a task. In practice, an estimate of the worst-case value (i. e., highest demand) would be used in assessments that incorporate task demands as reference values. In one form, such assessments can produce binary results. For example, availability can be equal to or exceed demand (resource sufficiency),or it can be less than demand (resource insuf­ficiency). These rule-based assessments are useful in identifying limiting factors, i. e., those performance resources that inhibit a specified type of task from being performed successfully or that prevent achieve­ment of a higher level of performance in a given type of task.

If RAk [bject y) Rd [task aQ then

Measurement Tools and Processes in Rehabilitation Engineering

Ra [bject yQ is insufficient

These rule-based assessments represent the basic process often applied (sometimes subliminally) by experienced clinicians in making routine decisions, as evidenced by statements such as "not enough strength," "not enough stability," etc. It is natural to extend and build on these strategies for use with objective measures. Extreme care must be employed. It is often possible, for example, for an individual to substitute another performance resource that is not insufficient for one that is. Clinicians take into account many such factors, and objective components should be combined with subjective assessments that provide the required breadth that enhances validity of objective components of a given assessment.

Using the same numeric values employed in rule-based binary assessments, a preference capacity stress metric can be computed:

RDk [task A) Ra [subject Y[


Performance capacity stress [)□

Measurement Tools and Processes in Rehabilitation Engineering



Binary assessments also can be made using this metric and a threshold of 100%. However, the stress value provides additional information regarding how far (or close) a given performance capacity value is from the sufficiency threshold.

Measurement Objectives and Approaches Characterizing the Human System and Its Subsystems

Figure 145.1 illustrates various points at which measurements are made over the course of a disease or injury, as well as some of the purposes for which they are made. The majority of measurements made in rehabilitation are aimed at characterizing the human system.

Measurements of human structure [e. g., Pheasant, 1986] play a critical role in the design and pre­scription of components such as seating, wheelchairs, workstations, artificial limbs, etc. Just like clothing, these items must "fit" the specific individual. Basic tools such as measuring tapes and rulers are becoming supplemented with three-dimensional digitizers and devices found in computer-aided manufacturing.

Measurement Tools and Processes in Rehabilitation Engineering

FIGURE 145.1 Measurements of structure, performance, and behavior serve many different purposes at different points over the course of a disease or injury that results in the need for rehabilitation services.

Measurements of structure (e. g., limb segment lengths, moments of inertia, etc.) are also used with computer models [Vasta & Knodraske, 1995] in the process of analyzing tasks to determine demands in terms of performance capacity variables associated with basic systems such s flexors and extensors.

After nearly 50 years, during which a plethora of mostly disease – and injury-specific functional assessment scales were developed, the functional independence measure (FIM) [Hamilton et al., 1987; Keith et al., 1987] is of particular note. It is the partial result of a task-force effort to produce a systematic methodology (Uniform Data System for Medical Rehabilitation) with the specific intent of achieving standardization throughout the clinical service-delivery system. This broad methodology uses subjective judgements exclusively, based on rigorous written guidelines, to categorize demographic, diagnostic, functional, and cost information for patients within rehabilitation settings. Its simplicity to use once learned and its relatively low cost of implementation have helped in gaining a rather widespread utilization for tracking progress of individuals from admission to discharge in rehabilitation programs and evaluating effectiveness of specific therapies within and across institutions.

In contrast, many objective measurement tools of varying degrees of technological sophistication exist [Jones, 1995; Kondraske, 1995, Smith & Leslie, 1990; Potvin et al, 1985; Smith, 1995] (also see Further Information below. A good fraction of these have been designed to accomplish the same purposes as corresponding subjective methods, but with increased resolution, sensitivity, and repeatability. The intent is not always to replace subjective methods completely but to make available alternatives with the advantages noted for situations that demand superior performance in the aspects noted. There are certain measurement needs, however, that cannot be accomplished via subjective means (e. g., measurement of a human’s visual information-processing speed, which involves the measurement of times of less than 1 second with millisecond resolution). These needs draw on the latest technology in a wide variety of ways, as demonstrated in the cited material.

With regard to instrumented measurements that pertain to a specific individual, performance capacity measures at both complex body system and basic system levels (Fig. 145.1) Constitute a major area of activity. A prime example is methodology associated with the implementation of industrial lifting stan­dards [NIOSH, 1981]. Performance-capacity measures reflect the limits of availability of one or more selected resources and require test strategies in which the subject is commanded to perform at or near a maximum level under controlled conditions. Performance tests typically last only a short time (seconds or minutes). To improve estimates of capacities, multiple trials are usually included in a given "test" from which a final measure is computed according to some established reduction criterion (e. g., average across five trials, best of three trials). This strategy also tends to improve test-retest repeatability. Performance capacities associated with basic and intermediate-level systems are important because they are "targets of therapy" [Tourtellotte, 1993], i. e., the entities that patients and service providers want to increase to enhance the chance that enough will be available to accomplish the tasks of daily life. Thus measurements of baseline levels and changes during the course of a rehabilitation program provide important docu­mentation (for medical, legal, insurance, and other purposes) as well as feedback to both the rehabilitation team and the patient.

Parameters of human behavior are also frequently acquired, often to help understand an individual’s response to a therapy or new circumstance (e. g., obtaining a new wheelchair or prosthetic device). Behavioral parameters reflect what the subject does normally and are typically recorded over longer time periods (e. g., hours or days) compared with that required for a performance capacity measurement under conditions that are more representative of the subject’s natural habitat (i. e., less laboratory-like). The general approach involves identifying the behavior (i. e., an event such as "head flexion," "keystrokes," "steps," "repositionings," etc.) and at least one parametric attribute of it. Frequency, with units of "events per unit time," and time spent in a given behavioral or activity state [e. g., Gonapthy & Kondraske, 1990] are the most commonly employed behavioral metrics. States may be detected with electromyographic means or electronic sensors that respond to force, motion, position, or orientation. Behavioral measures can be used as feedback to a subject as a means to encourage desired behaviors or discourage undesirable behaviors.

Task characterization or task analysis, like the organization of human system parameters, is facilitated with a hierarchical perspective. A highly objective, algorithmic approach could be delineated for task analysis in any given situation [Imrhan, 1995; Maxwell, 1995]. The basic objective is to obtain both descriptive and quantitative information for making decisions about the interface of a system (typically a human) to a given task. Specifically, function, procedures, and goals are of special interest. Function represents the purpose of a task (e. g., to flex the elbow, to lift an object, to communicate). In contrast, task goals relate to performance, or how well the function is to be excuted, and are quantifiable (e. g., the mass of an object to be lifted, the distance over which the lift must occur, the speed at which the lift must be performed, etc.). In situations with human and artificial systems, the term overall task goals is used to distinguish between goals of the combined human-artificial system and goals associated with the task of operating the artificial system. Procedures represent the process by which goals are achieved. Characterization of procedures can include descriptive and quantitative components (e. g., location of a person’s hands at beginning and end points of a task, path in three-dimensional space between beginning and end points). Partial or completely unspecified procedures allow for variations in style. Goals and procedures are used to obtain numeric estimates of task demands in terms of the performance resources associated with the systems anticipated to be used to execute the task. Task demands are time dependent. Worst-case demands, which may occur only at specific instants in time, are of primary interest in task analysis.

Estimates of task demand can be obtained (1) direct measurement (i. e., of goals and procedures),

The use of physics-based models to map direct measurements into parameters that relate more readily to measurable performance capacities of human subsystems, or (3) inference. Examples of direct mea­surement include key dimensions and mass of objects, three-dimensional spatial locations between "beginning" and "end points" of objects in tasks involving the movement of objects, etc. Instrumentation supporting task analysis is available (e. g., load cells, video and other systems for measuring human position and orientation in real-time during dynamic activities), but it is not often integrated into systems for task analysis per se. Direct measurements of forces based on masses of objects and gravity often must be translated (to torques about a given body joint): this requires the use of static and dynamic models and analysis [e. g., Vista & Kondraske, 1995; Winter, 1990].

An example of an inferential task-analysis approach that is relatively new is nonlinear causal resource analysis (NCRA)[Kondraske, 1999; Kondraske et al., 1997; Kondraske, 1988]. This method was motivated by human performance analysis situations where direct analysis is not possible (e. g., determination of the amount of visual information-processing speed required to drive safely on a highway). Quantitative task demands, in terms of performance variables that characterize the involved subsystems, are inferred from a population data set that includes measures of subsystem performance, resource availabilities (e. g., speed, accuracy, etc.), and overall performance on the task in question. This method is based on the simple observation that the individual with the least amount of the given resource (i. e., the lowest performance capacity) who is still able to accomplish a given goal (i. e., achieve a given level of performance in the specified high-level task) provides the key clue. That amount of availability is used to infer the amount of demand imposed by the task.

The ultimate goal to which task characterization contributes is to identify limiting factors or unsafe conditions when a specific subject undertakes the task in question; this goal must not be lost while carrying out the basic objectives of task analysis. While rigorous algorithmic approaches are useful to make evident the true detail of the process, they are generally not performed in this manner in practice at present. Rather, the skill and experience of individuals performing the analysis are used to simplify the process, resulting in a judicious mixture of subjectives estimates and objective measurements. For example, some limiting factors (e. g., grip strength) may be immediately identified without measurement of the human or the task requirements because the margin between availability and demand is so great that quick subjective "measurements" followed by an equally quick "assessment" can be used to arrive at the proper conclusion (e. g., "grip strength is a limiting factor in this task").

Assistive devices can be viewed as artificial systems that either completely or partially bridge a gap between a given human (with his or her unique profile of performance capacities, i. e., available performance resources) and a particular task or class of tasks (e. g., communication, mobility, etc.). It is thus possible to consider the aspects of the device that constitute the user-device interface and those aspects which constitute, more generally, the device-task interface. In general, measurements supporting assessment of the user-device interface can be viewed to consist of (1) those which characterize the human and (2) those which characterize tasks (i. e., "operating" the assistive device). Each of these was described earlier. Measurements that characterize the device-task interface are often carried out in the context of the complete system, i. e., the human-assistive device-task combination (see next subsection).

Characterizing Overall Systems in High-Level-Task Situations

This situation generally applies to a human-artificial system-task combination. Examples include an individual using a communication aid to communicate, an individual using a wheelchair to achieve mobility, etc. Here, concern is aimed at documenting how well the task (e. g., communication, mobility, etc.) is achieved by the composite or overall system. Specific aspects or dimensions of performance associated with the relevant function should first be identified. Examples include speech, accuracy, stability, efficiency, etc. The total system is then maximally challenged (tempered by safety considerations) to operate along one or more of these dimensions of performance (usually not more than two dimensions are maximally challenged at the same time). For example, a subject with a communication device may be challenged to generate a single selected symbol "as fast as possible" (stressing speed without concern for accuracy). Speed is measured (e. g., with units of symbols per second) over the course of short trial (so as not to be influenced by fatigue). Then the "total system" may be challenged to generate a subset of specific symbols (chosen at random from the set of those available with a given device) one at a time, "as accurately as possible" (stressing accuracy while minimizing stress on speed capacities). Accuracy is then measured after a representative number of such trials are administered (in terms of "percent correct," for example). To further delineate the speed-accuracy performance envelope, "the system" may be chal­lenge to select symbols at a fixed rate while accuracy is measured. Additional dimensions can be evaluated similarly. For example, endurance (measure in units of time) can be determined by selecting an operating point (e. g., by reference to the speed-accuracy performance envelope) and challenging the total system "to communicate" for "as long as possible" under the selected speed-accuracy condition.

In general, it is more useful if these types of characterizations consider all relevant dimensions with some level of measurements (i. e., subjective or objective) than it would be to apply a high resolution, objective measurement in a process that considers only one aspect of performance.

Decision-Making Processes

Measurements that characterize the human, task, assistive device, or combination thereof are themselves only means to an end; the end is typically a decision. As noted previously, decisions are often the result of assessment processes involving one or more measurements. Although not exhaustive, many of the different types of assessments encountered are related to the following questions: (1) Is a particular aspect of performance normal (or impaired)? (2) Is a particular aspect of performance improving, stable, or getting worse? How should therapy be modified? (3) Can a given subject utilize (and benefit from) a particular assistive device? (4) Does a subject possess the required capacity to accomplish a given higher level task (e. g., driving, a particular job after a work-related injury, etc.)?

In Fig. 145.2, several of the basic concepts associated with measurement are used to illustrate how they enter into and facilitate systematic decision-making processes. The upper section shows raw score values as well as statistics for a healthy normal reference population in tabular form (left). It is difficult to reach any decision by simple inspection of just the raw performance capacity values. Tabular data are used to obtain percent normal (middle) and z-score (right) assessments. Both provide a more directly interpret­able result regarding subject A’s impairments. By examining the "right shoulder flexion extreme of motion" item in the figure, it can be seen that a raw score value corresponding to 51.2% normal yields a very large-magnitude, negative z-score (-10.4). This z-score indicates that virtually no one in the reference population would have a score this low. In contrast, consider similar scores for the "grip strength" item (56.2% normal, z-score = -1.99). On the basis of percent normal scores, it would appear that both of these resources are similarly affected, whereas the z-score basis provides a considerably different perspective due to the fact that grip strength is much more variable in healthy populations than the extreme angle obtained by a given limb segment about a joint, relatively speaking. As noted, z-scores account for this variability.

The lower section of Fig. 145.2 considers a situation in which the issue is a specific individual (subject A) considered in a specific task. Tabular data now include raw score values (which are the same as in upper section of the figure) and quantitative demands (typically worst case) imposed on the respective perfor­mance resources by task X. The lower-middle plot illustrates the process of individually assessing suffi­ciency of each performance resource in this task context using a rule-based assessment that incorporates the idea of a threshold (i. e., availability must exceed demand for sufficiency). The lower-right plot illustrates an analogous assessment process that is executed after computation of a stress metric for each of the performance capacities. Here, any demand that corresponds to more than a 100% stress level is obviously problematic. In addition to binary conclusions regarding whether a given capacity is or is not a limiting factor, it is possible to observe that of the two limiting resources (e. g., grip strength and right shoulder flexion extreme of motion), the former is more substantial. This might suggest, for example, that the task be modified so as to decrease the grip-strength demand (i. e., gains in performance capacity required would be substantial to achieve sufficiency) and the use of focused exercise therapy to increase shoulder flexion mobility (i. e., gains in mobility required are relatively small).

145.4 Current Limitations

Quality of Measurements

Key issues are measurement validity, reliability (or repeatability), accuracy, and discriminating power. At issue in terms of current limitations is not necessarily the quality of measurements but limitations with regard to methods employed to determine the quality of measurements and their interpretability.

A complete treatment of these complex topics is beyond the present scope. However, it can be said that standards are such [Potvin et al., 1985] that most published works regarding measurement instru­ments do address quality of measurements to some extent. Validity (i. e., how well does the measurement reflect the intended quantity) and reliability are most often addressed. However, one could easily be left with the impression that these are binary conditions (i. e., measurement is or is not reliable or valid), when in fact a continuum is required to represent these constructs. Of all attributes that relate to measurement quality, reliability is most commonly expressed in quantitative terms. This is perhaps because statistical methods have been defined and promulgated for the computation of so-called reliability coefficients [Winer, 1971]. Reliability coefficients range from 0.0 to 1.0, and the implication is that 1.0 indicates a perfectly reliable or repeatable measurement process. Current methods are adequate, at best, for making inferences regarding the relative quality of two or more methods of quantifying "the same thing." Even these comparisons require great care. For example, measurement instruments that have greater intrinsic resolving power have a great opportunity to yield smaller-reliability coefficients simply because they are capable of measuring the true variability (on repeated measurement) of the parameter in question within the system under test. While there has been widespread determination or reliability coefficients, there has been little or no effort directed toward determination of what value of a reliability coefficient is "good enough" for a particular application. In fact, reliability coefficients are relatively abstract to most practitioners.

© 2000 by CRC Press LLC


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Visual Acuity (1/Visual angle in minutes) ♦





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Measurement Tools and Processes in Rehabilitation Engineering
подпись: © 2000 by crc press llc
Measurement Tools and Processes in Rehabilitation Engineering
Measurement Tools and Processes in Rehabilitation Engineering

Subject’s Resource Availabilities and Task Demaids as Percentage of Normal Population Mean Availability


Measurement Tools and Processes in Rehabilitation Engineering Measurement Tools and Processes in Rehabilitation Engineering

FIGURE 145.2 Examples of different types of assessments that can be performed by combining performance capacity measures and reference values of different types. The upper section shows raw score values as well as statistics for a healthy normal reference population in tabular form (left). It is difficult to reach any decision by simple inspection of just the raw performance capacity values. Tabular data are used to obtain a percent normal assessment (middle) and a z-score assessment (right). Both of these provide a more directly interpretable result regarding subject A’s impairments. The lower section shows raw score values (same as in upper section) and quantitative demands (typically worst case) imposed on the respective performance resources by task X. The lower-middle plot illustrates the process of individually assessing sufficiency of each performance resource in this task context using a threshold rule (i. e., availability must exceed demand for sufficiency). The lower-right plot illustrates a similar assessment process after computation of a stress metric for each of the performance capacities. Here, any demand that corresponds to more than a 100% stress level is obviously problematic.

Methods for determining the quality of a measurement process (including the instrument, procedures, examiner, and actual noise present in the variable of interest) that allow a practitioner to easily reach decisions regarding the use of a particular measurement instrument in a specific application and limi­tations thereof are currently lacking. At the use of different measurements increases and the number of options available for obtaining a given measurement grows, this topic will undoubtedly receive additional attention. Caution in interpreting literature, common sense, and the use of simple concepts such as "I need to measure range of motion to within 2 degrees in my application" are recommended in the meantime [Mayer et al., 1997].


Measurements, and concepts with which they are associated, can contribute to a shift from experience – based knowledge acquisition to rule-based, engineering-like methods. This requires (1) a widely accepted conceptual framework (i. e. known to assistive device manufacturers, rehabilitation engineers, and other professionals within the rehabilitation community), (2) a more complete set of measurement tools that are at least standardized with regard to the definition of the quantity measured, (3) special analysis and assessment software (that removes the resistance to the application of more rigorous methods by enhanc­ing the quality of decisions as well as the speed with which they can be reached), and (4) properly trained practitioners. Each is a necessary but not sufficient component. Thus balanced progress is required in each of these areas.

Rehabilitation Service Delivery and Rehabilitation Engineering

In a broad sense, it has been argued that all engineers can be considered rehabilitation engineers who merely work at different levels along a comprehensive spectrum of human performance, which itself can represent a common denominator among all humans. Thus an automobile is a mobility aid, a telephone is a communication aid, and so on. Just as in other engineering disciplines, measurement must be recognized not only as an important end in itself (in appropriate instances) but also as an integral component or means within the overall scope of rehabilitation and rehabilitation engineering processes. The service-delivery infrastructure must provide for such means. At present, one should anticipate and be prepared to overcome potential limitations associated with factors such as third-party reimbursement for measurement procedures, recognition of equipment and maintenance costs associated with obtaining engineering-quality measurements, and education of administrative staff and practitioners with regard to the value and proper use of measurements.

Defining Terms




Information). Others (e. g., the human arm) map to multiple functions, although at any given time multifunction systems are likely to be executing a single function (e. g., polishing a car). Functions can be described and inventoried, whereas level of performance of a given function can be measured. Functional assessment: The process of determining, from a relatively global perspective, an individual’s

Ability to carry out tasks in daily life. Also, the result of such a process. Functional assessments typically cover a range of selected activity areas and include (at a minimum) a relatively gross indication (e. g., can or can’t do; with or without assistance) of status in each area.


Which is specified. Examples include specified task goals (e. g., move an object of specified mass from point A to point B in 3 seconds) or estimated task performance (maximum mass, range, speed of movement obtainable given a specified elemental performance resource availability pro­file), depending on whether a reverse or forward analysis problem is undertaken. Whereas function describes the general process of task, the goal directly relates to performance and is quantitative.


Flexor speed) that is available in an amount that is less than the worst-case demand imposed by a task. Thus a given resource can be "limiting" only when considered in the context of a specific task.

Overall task goals: Goals associated with a task to be executed by a human-artificial system combination

(to be distinguished from goals associated with the task of operating the artificial system).

Performance: Unique qualities of a human or artificial system (e. g., strength, speed, accuracy, endur­



Drawn on during tasks, and limits some aspect (e. g., speed, force, production, etc.) of a system’s ability to execute tasks, or, the limit of that aspect itself.

Performance capacity measurement: A general class of measurements, performed at different hierar­

Chical levels, intended to quantify one or more performance capacities.


Set of goals) associated with a given function can be achieved. Procedure specification requires specification of initial intermediate, and/or final states or conditions dictating how the goal is to be accomplished. Such specification can be thought of in terms of removing some degrees of freedom.

Structure: Physical manifestation and attributes of a human or artificial system and the object of one

Type of measurements at multiple hierarchical levels.


Of a procedure or resulting from either international or unintentional incomplete specification of procedure.




Fuhrer MJ. 1987. Rehabilitation Outcomes: Analysis and Measurement. Baltimore, Brookes.

Ganapathy G, Kondraske GV. 1990. Microprocessor-based instrumentation for ambulatory behavior monitoring. J Clin Eng 15(6):459.

Granger CV, Greshorn GE. 1984. Functional Assessment in Rehabilitation Medicine. Baltimore, Williams & Wilkins.

Hamilton BB, Granger CV, Sherwin FS, et al. 1987. A uniform national data system for medical rehabil­itation. In MJ Fuhrer (ed), Rehabilitation Outcomes: Analysis and Measurement, pp 137-147. Baltimore, Brookes.

Imrhan S. 2000. Task analysis and decomposition: Physical components. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Jones RD. 1995. Measurement of neuromotor control performance capacities. In JD Bronzino (ed), Handbook of Biomedical Engineering. Boca Raton, Fla, CRC Press.

Keith RA, Granger CV, Hamilton BB, Sherwin FS. 1987. The functional independence measure: A new tool for rehabilitation. In MG Eisenberg, RC Grzesiak (eds), Advances in Clinical Rehabilitation, vol 1, pp 6-18. New York, Springer-Verlag.

Kondraske GV. 1988. Experimental evaluation of an elemental resource model for human performance. In Proceedings of the Tenth Annual IEEE Engineering in Medicine and Biology Society Conference, New Orleans, pp 1612-13.


Kondraske GV. 2000. A working model for human system-task interfaces. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Kondraske GV, Johnston C, Pearson, A & Tarbox L. 1997. Performance Prediction and limiting resource identification with nonlinear causal resource analysis. Proceedings, 19th Annual Engineering in Medicine and Biology Society Conference, pp 1813-1816.

Kondraske GV, Vasta PJ. 2000. Measurement of information processing performance capacities. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Maxwell KJ. 2000. High-level task analysis: Mental components. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Mayer T, Kondraske GV, Brady Beals, S., & Gatchel RJ. 1997. Spinal range of motion: accuracy and sources of error with inclinometric measurement. Spine, 22(17), 1976-1984.

National Institute of Occupational Safety and Health (NIOSH). 1981. Work Practices Guide for Manual Lifting (DHHS Publication No. 81122). Washington, US Government Printing Office.

Pheasant ST. 1986. Bodyspace: Anthropometry, Ergonomics and Design. Philadelphia, Taylor & Francis.

Potvin AR, Tourtellotte WW, Potvin JH, et al. 1985. The Quantitative Examination of Neurologic Func­tion. Boca Raton, Fla, CRC Press.

Smith RV, Leslie JH. 1990. Rehabilitation Engineering. Boca Raton, Fla, CRC Press.

Smith SS. 2000. Measurement of neuromuscular performance capacities. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Tourtellotte WW. 1993. Personal communication.


Vasta PJ, Kondraske GV. 2000. Human performance engineering: Computer based design and analysis tools. In JD Bronzino (ed), Handbook of Biomedical Engineering, 2nd ed., Boca Raton, Fla, CRC Press.

Webster JG, Cook AM, Tompkin WJ, Vanderheiden GC. 1985. Electronic Devices for Rehabilitation. New York, Wiley.

Winer BJ. 1971. Statistical Principles in Experimental design, 2d ed. New York, McGraw-Hill.

Winter DA. 1990. Biomechanics and Motor Control of Human Movement, 2d ed. New York, Wiley.

World Health Organization. 1980. International Classification of Impairments, Disabilities, and Handi­caps. Geneva, World Health Organization.

Further Information

The section of this Handbook entitled "Human Performance Engineering" contains chapters that address

Human performance modeling and measurement in considerably more detail.

Manufacturers of instruments used to characterize different aspects of human performance often provide

Technical literature and bibliographies with conceptual backgrounds, technical specifications and application

Examples. A partial list of such sources is included below. (No endorsement of products is implied.)

Baltimore Therapeutic Equipment Co.

7455-L New Ridge Road Hanover, MD 21076-3105 Http://www. bteco. com/

Chattanooga Group 4717 Adams Road Hixson, TN 37343 Http://www. chattanoogagroup. com/

Henley Healthcare 120 Industrial Blvd.

Sugarland, TX 77478 Http://www. henleyhealth. com/

Human Performance Measurement, Inc.

P. O. Box 1996

Arlington, TX 76004-1996

Http://www. flash. net/~hpm/

Lafayette Instrument 3700 Sagamore Parkway North Lafayette, IN 47904-5729 Http://www. lafayetteinstrument. com

The National Institute on Disability and Rehabilitation Research (NIDRR), part of the Department of Education, funds a set of Rehabilitation Engineering Research Centers (RERCs) and Research and Training Centers (RTCs). Each has a particular technical focus; most include measurements and mea­surements issues. Contact NIDRR for a current listing of these centers.

Measurement devices, issues, and application examples specific to rehabilitation are included in the following journals:

IEEE Transactions on Rehabilitation Engineering IEEE Service Center 445 Hoes Lane P. O. Box 1331

Piscataway, N. J. 08855-1331 Http://www. ieee. org/index. html

Journal of Rehabilitation Research and Development Scientific and Technical Publications Section Rehabilitation Research and Development Service 103 South Gay St., 5th floor Baltimore, MD 21202-4051 Http://www. vard. org/jour/jourindx. htm

Archives of Physical Medicine and Rehabilitation Suite 1310

78 East Adams Street Chicago, IL 60603-6103

American Journal of Occupational Therapy

The American Occupational Therapy Association, Inc.

4720 Montgomery Ln.,

Bethesda, MD 20814-3425 Http://www. aota. org/

Physical Therapy

American Physical Therapy Association 1111 North Fairfax St.

Alexandria, VA 22314 Http://www. apta. org/

Journal of Occupational Rehabilitation Subscription Department Plenum Publishing Corporation 233 Spring St.

New York, NY 10013 Http://www. plenum. com/

Hobson, D., Trefler, E. “Rehabilitation Engineering Technologies: Principles of Application.” The Biomedical Engineering Handbook: Second Edition.

Ed. Joseph D. Bronzino

Boca Raton: CRC Press LLC, 2000

Electrical network

An electrical network is an interconnection of electrical elements such as resistors, inductors, capacitors, transmission lines, voltage sources, current sources, and switches.

An electrical circuit is a network that has a closed loop, giving a return path for the current. A network is a connection of two or more components, and may not necessarily be a circuit.


A network that also contains active electronic components is known as an electronic circuit. Such networks are generally nonlinear and require more complex design and analysis tools.