Machine-based Multimodal Pain Assessment Tool for Infants: A Review
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paper_title: Specific Newborn Individualized Developmental Care and Assessment Program movements are associated with acute pain in preterm infants in the neonatal intensive care unit.

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OBJECTIVE ::: The Newborn Individualized Developmental Care and Assessment Program (NIDCAP) is widely used in neonatal intensive care units and comprises 85 discrete infant behaviors, some of which may communicate infant distress. The objective of this study was to identify developmentally relevant movements indicative of pain in preterm infants. ::: ::: ::: METHODS ::: Forty-four preterm infants were assessed at 32 weeks' gestational age (GA) during 3 phases (baseline, lance/squeeze, and recovery) of routine blood collection in the neonatal intensive care unit. The NIDCAP and Neonatal Facial Coding System (NFCS) were coded from separate continuous bedside video recordings; mean heart rate (mHR) was derived from digitally sampled continuous electrographic recordings. Analysis of variance (phase x gender) with Bonferroni corrections was used to compare differences in NIDCAP, NFCS, and mHR. Pearson correlations were used to examine relationships between the NIDCAP and infant background characteristics. ::: ::: ::: RESULTS ::: NFCS and mHR increased significantly to lance/squeeze. Eight NIDCAP behaviors also increased significantly to lance/squeeze. Another 5 NIDCAP behaviors decreased significantly to lance/squeeze. Infants who had lower GA at birth, had been sicker, had experienced more painful procedures, or had greater morphine exposure showed increased hand movements indicative of increased distress. ::: ::: ::: CONCLUSIONS ::: Of the 85 NIDCAP behaviors, a subset of 8 NIDCAP movements were associated with pain. Particularly for infants who are born at early GAs, addition of these movements to commonly used measures may improve the accuracy of pain assessment.
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paper_title: Neonatal pain in relation to postnatal growth in infants born very preterm

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Procedural pain is associated with poorer neurodevelopment in infants born very preterm (≤ 32 weeks gestational age), however, the etiology is unclear. Animal studies have demonstrated that early environmental stress leads to slower postnatal growth; however, it is unknown whether neonatal pain-related stress affects postnatal growth in infants born very preterm. The aim of this study was to examine whether greater neonatal pain (number of skin-breaking procedures adjusted for medical confounders) is related to decreased postnatal growth (weight and head circumference [HC] percentiles) early in life and at term-equivalent age in infants born very preterm. Participants were n=78 preterm infants born ≤ 32 weeks gestational age, followed prospectively since birth. Infants were weighed and HC measured at birth, early in life (median: 32 weeks [interquartile range 30.7-33.6]) and at term-equivalent age (40 weeks [interquartile range 38.6-42.6]). Weight and HC percentiles were computed from sex-specific British Columbia population-based data. Greater neonatal pain predicted lower body weight (Wald χ(2)=7.36, P=0.01) and HC (Wald χ(2)=4.36, P=0.04) percentiles at 32 weeks postconceptional age, after adjusting for birth weight percentile and postnatal risk factors of illness severity, duration of mechanical ventilation, infection, and morphine and corticosteroid exposure. However, later neonatal infection predicted lower weight percentile at term (Wald χ(2)=5.09, P=0.02). Infants born very preterm undergo repetitive procedural pain during a period of physiological immaturity that appears to impact postnatal growth, and may activate a downstream cascade of stress signaling that affects later growth in the neonatal intensive care unit.
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paper_title: Gender is a confounding factor in pain trials: women report more pain than men after arthroscopic surgery

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Abstract A gender difference in the incidence of acute pain may be a confounder in analgesic trials. We have tested the hypothesis that the incidence of acute pain after knee arthroscopic procedures is greater in women than men. We performed three RCTs on intra-articular analgesics in which no postoperative analgesia was given until the need for such treatment was documented by scoring moderate-to-severe pain on a verbal rating scale (VRS 0–4; n =219), and a 0–100 mm visual analogue pain scale (VAS) within 2 h postoperatively. All trials were performed with an intra-articular catheter technique. The design allowed us to study the natural course of pain after arthroscopic surgery until analgesia was required. Women reported more pain of at least moderate intensity than men (84 vs 57%; P
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paper_title: Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

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Background ::: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. ::: ::: ::: Methods ::: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. ::: ::: ::: Results ::: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. ::: ::: ::: Conclusion ::: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.
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paper_title: Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation

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The physical activity in normal daily life is determined to a large extent by the functional ability of a subject. As a result, the measurement of the physical activity that a subject performs spontaneously could be a useful and objective measurement of disability, particularly in patients with disease-related functional impairment. The aim of this study is to provide an accurate method for the measurement and analysis of the physical activity under normal life conditions. Using three kinematical sensors strapped to the body, both the posture and the gait parameters can be assessed qualitatively and quantitatively. A detailed description of the algorithms used to analyse both the posture and the gait are presented in this paper. Two methods, based on different sensor configurations and signal processing, are proposed for the detection of sitting and standing postures (Methods P1 and P2). Two other methods are used for the quantitative assessment of walking (Methods W1 and W2). The performance of the algorithms (expressed in terms of sensitivity, specificity and error) is based on the comparison of data recorded simultaneously by a non-interfering observer (reference data) with the data provided by the recording system (21 patients, 61 h). Sensitivity and specificity are respectively 98.2% and 98.8% (P1), 97.8% and 98.1% (P2) for sitting; 98.0% and 98.5% (P1), 97.4% and 97.8% (P2) for standing; 97.1% and 97.9% (W1), 92.4% and 94.9% (W2) for walking; and finally, 99.2% and 98.6% for lying. Overall detection errors (as a percent of range) are as follows: 1.15% (P1) and 1.20% (P2) for sitting, 1.36% (P1) and 1.40% (P2) for standing, 1.20% (W1) and 1.60% (W2) for walking and 0.40% for lying. The error for the estimated walking distance and the speed is 6.8% and 9.6%, respectively. We conclude that both methods can be used for the accurate measurement of the basic physical activity in normal daily life. Measurements performed before and after the delivery of a treatment can therefore provide information of unprecedented accuracy and objectivity on the ability of a procedure, in this case spinal cord stimulation, to restore functional capabilities.
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paper_title: Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables

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� Abstract— A stress detection system is developed based on the physiological signals monitored by non-invasive and non-intrusive sensors. The development of this emotion recognition system involved three stages: experiment setup for physiological sensing, signal preprocessing for the extraction of affective features and affective recognition using a learning system. Four signals: Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), Pupil Diameter (PD) and Skin Temperature (ST) are monitored and analyzed to differentiate affective states in a computer user. A Support Vector Machine is used to perform the supervised classification of affective states between "stress" and "relaxed". Results indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in emotional state of our experimental subjects when stress stimuli are applied to the interaction environment. It was also found that the pupil diameter was the most significant affective state indicator, compared to the other three physiological signals monitored.
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paper_title: Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain

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We present initial results from the application of an automated facial expression recognition system to spontaneous facial expressions of pain. In this study, 26 participants were videotaped under three experimental conditions: baseline, posed pain, and real pain. In the real pain condition, subjects experienced cold pressor pain by submerging their arm in ice water. Our goal was to automatically determine which experimental condition was shown in a 60 second clip from a previously unseen subject. We chose a machine learning approach, previously used successfully to categorize basic emotional facial expressions in posed datasets as well as to detect individual facial actions of the Facial Action Coding System (FACS) (Littlewort et al, 2006; Bartlett et al., 2006). For this study, we trained 20 Action Unit (AU) classifiers on over 5000 images selected from a combination of posed and spontaneous facial expressions. The output of the system was a real valued number indicating the distance to the separating hyperplane for each classifier. Applying this system to the pain video data produced a 20 channel output stream, consisting of one real value for each learned AU, for each frame of the video. This data was passed to a second layer of classifiers to predict the difference between baseline and pained faces, and the difference between expressions of real pain and fake pain. Naive human subjects tested on the same videos were at chance for differentiating faked from real pain, obtaining only 52% accuracy. The automated system was successfully able to differentiate faked from real pain. In an analysis of 26 subjects, the system obtained 72% correct for subject independent discrimination of real versus fake pain on a 2-alternative forced choice. Moreover, the most discriminative facial action in the automated system output was AU 4 (brow lower), which all was consistent with findings using human expert FACS codes.
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paper_title: Agitation and pain assessment using digital imaging

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Pain assessment in patients who are unable to verbally communicate with medical staff is a challenging problem in patient critical care. The fundamental limitations in sedation and pain assessment in the intensive care unit (ICU) stem from subjective assessment criteria, rather than quantifiable, measurable data for ICU sedation and analgesia. This often results in poor quality and inconsistent treatment of patient agitation and pain from nurse to nurse. Recent advancements in pattern recognition techniques using a relevance vector machine algorithm can assist medical staff in assessing sedation and pain by constantly monitoring the patient and providing the clinician with quantifiable data for ICU sedation. In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners.
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paper_title: The Faces Pain Scale – Revised: toward a common metric in pediatric pain measurement

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The Faces Pain Scale (FPS; Bieri et al., Pain 41 (1990) 139) is a self-report measure used to assess the intensity of children's pain. Three studies were carried out to revise the original scale and validate the adapted version. In the first phase, the FPS was revised from its original seven faces to six, while maintaining its desirable psychometric properties, in order to make it compatible in scoring with other self-rating and observational scales which use a common metric (0-5 or 0-10). Using a computer-animated version of the FPS developed by Champion and colleagues (Sydney Animated Facial Expressions Scale), psychophysical methods were applied to identify four faces representing equal intervals between the scale values representing least pain and most pain. In the second phase, children used the new six-face Faces Pain Scale-Revised (FPS-R) to rate the intensity of pain from ear piercing. Its validity is supported by a strong positive correlation (r=0.93, N=76) with a visual analogue scale (VAS) measure in children aged 5-12 years. In the third phase, a clinical sample of pediatric inpatients aged 4-12 years used the FPS-R and a VAS or the colored analogue scale (CAS) to rate pain during hospitalization for surgical and non-surgical painful conditions. The validity of the FPS-R was further supported by strong positive correlations with the VAS (r=0.92, N=45) and the CAS (r=0.84, N=45) in this clinical sample. Most children in all age groups including the youngest were able to use the FPS-R in a manner that was consistent with the other measures. There were no significant differences between the means on the FPS-R and either of the analogue scales. The FPS-R is shown to be appropriate for use in assessment of the intensity of children's acute pain from age 4 or 5 onward. It has the advantage of being suitable for use with the most widely used metric for scoring (0-10), and conforms closely to a linear interval scale.
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paper_title: Judgments of infant pain: the impact of caregiver identity and infant age.

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OBJECTIVE ::: To examine whether caregiver judgments of infant pain would vary systematically with different infant caregiver groups and infant age. ::: ::: ::: METHODS ::: A total of 123 caregivers (41 parents, 41 in patient nurses, 41 pediatricians) viewed videotapes of the vigorous behavioral responses of healthy infants (aged 2, 4, 6, 12, and 18 months) to a routine immunization injection and provided ratings of both the affective distress and pain intensity observed. ::: ::: ::: RESULTS ::: A principal components analysis of affective and intensity ratings yielded a weighted pain summary score for each injection event. Older infants were attributed significantly more pain than younger infants, even though the vigor of the behavioral reactions was experimentally controlled across age groups. A profile analysis contrasting observer groups indicated that pediatricians attributed significantly lower levels of pain than parents, while nurses were intermediate to the other groups, not significantly differing from either group. These systematic differences in judgments were consistent across infant age groups. ::: ::: ::: CONCLUSIONS ::: The findings reveal systematic sources of significant variations in observer judgments of infant pain. Despite an absence of differences in the behavioral reactions of the children, both the type of caregiver and their knowledge of the child's age systematically influenced attributions of pain to infants. This work suggests the important role of caregiver role variation and perceived developmental maturity as determinants of infant pain judgments and highlights potential areas of difficulty in controlling the unnecessary suffering of infants.
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paper_title: Spontaneous pain expression recognition in video sequences

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Automatic recognition of Pain expression has potential medical significance. In this paper we present results of the application of an automatic facial expression recognition system on sequences of spontaneous Pain expression. Twenty participants were videotaped while undergoing thermal heat stimulation at nonpainful and painful intensities. Pain was induced experimentally by use of a Peltierbased, computerized thermal stimulator with a 3 × 3 cm2 contact probe. Our aim is to automatically recognize the videos where Pain was induced. We chose a machine learning approach, previously used successfully to categorize the six basic facial expressions in posed datasets [1, 2] based on the Transferable Belief Model. For this paper, we extended this model to the recognition of sequences of spontaneous Pain expression. The originality of the proposed method is the use of the dynamic information for the recognition of spontaneous Pain expression and the combination of different sensors: facial features behavior, transient features and the context of the expression study. Experimental results show good classification rates for spontaneous Pain sequences especially when we use the contextual information. Moreover the system behaviour compares favourably to the human observer in the other case, which opens promising perspectives for the future development of the proposed system.
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paper_title: Innovating in pain assessment of the critically ill: exploring cerebral near-infrared spectroscopy as a bedside approach.

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Abstract Nurses play a crucial role in the evaluation and treatment of pain in the critically ill patient. This responsibility is all the more critical with this particular population because many may not be able to self-report their pain level and the typical behavioral signs of pain may be subtle or absent. According to recent recommendations, vital signs should not be used as primary indicators of pain but rather considered as a cue to begin further assessment. Other than vital signs, human brain reactivity to pain has been extensively studied with the use mainly of magnetic resonance imaging and positron-emission tomography. However, the use of these sophisticated methods may be unrealistic in the critically ill. Of interest to assessing these patients in a clinical setting is the noninvasive measurement of regional cerebral tissue oxygenation with the near-infrared spectroscopy (NIRS) technique. There are indications that NIRS is capable of detecting the cerebral hemodynamic changes associated with sensory stimuli, including pain. The objective of this review paper is to provide nurses with a better understanding of NIRS technology, including a review of the literature on functional studies that have used NIRS in critically ill populations, and how it could be used in both research and practice. Current NIRS techniques have well recognized limitations which must be considered carefully during the measurement and interpretation of signals. Thus, its clinical use is yet to be fully established. Nonetheless, cerebral NIRS technique as an approach to assess brain activity in response to pain should not be abandoned.
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paper_title: Pain Expression Recognition Based on pLSA Model

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We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: "no pain," "slight pain," "moderate pain," and " severe pain." First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness.
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paper_title: The faces pain scale for the self-assessment of the severity of pain experienced by children: Development, initial validation, and preliminary investigation for ratio scale properties

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Sununary Altogether 553 children (195 first graders, mean age 6.8 years, and 358 third graders, mean age 8.7 years) participated in the development of a self-report measure to assess the intensity of children's pain. The first step was the derivation, from children's drawings of facial expressions of pain, of 5 sets of 7 schematic faces depicting changes in severity of expressed pain from no pain to the most pain possible. With the set of faces that achieved the highest agreement in pain ordering, additional studies were conducted to determine whether the set had the properties of a scale. In one study, children rank-ordered the faces on 2 occasions, separated by 1 week. All 7 faces were correctly ranked by 64% (retest 1 week later, 61%) of grade 1 children and by 86% (reiest 89%) of grade 3 children. In a second study, the faces were presented in all possible paired combinations. All 7 faces were correctly placed by 62% (retest 86%) of the younger arid by 75% (retest 71%) of the older subjects. A third study asked children to place faces along a scale: a procedure allowing a check on the equality of intervals. The fourth study checked on whether pain was acting as an underlying construct for ordering the faces in memory. We asked whether children perceived the set as a scale by asking if memory for an ordered set of faces was more accurate than for a random set. The final study checked, with 6-ycm'-old children, the test-retest reliability of ratings for recalled experiences of pain. Overall, the faces pain scale incorporates conventions used by children, has achieved strong agreement in the rank ordering of pain, has indications that the intervals are close to equal, and is treated by children as a scale. The test-retest data suggest that it may prove to be a reliable index over time of self-reported pain.
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paper_title: Pain in the preterm neonate: behavioural and physiological indices

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The impact of invasive procedures on preterm neonates has received little systematic attention. We examined facial activity, body movements, and physiological measures in 56 preterm and full-term newborns in response to heel lancing, along with comparison preparatory and recovery intervals. The measures were recorded in special care and full-term nurseries during routine blood sampling. Data analyses indicated that in all measurement categories reactions of greatest magnitude were to the lancing procedure. Neonates with gestational ages as short as 25-27 weeks displayed physiological responsivity to the heel lance, but only in the heart rate measure did this vary with gestational age. Bodily activity was diminished in preterm neonates in general, relative to full-term newborns. Facial activity increased with the gestational age of the infant. Specificity of the response to the heel lance was greatest on the facial activity measure. Identification of pain requires attention to gestational age in the preterm neonate.
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paper_title: Digital Low-Pass Differentiation for Biological Signal Processing

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Digital low-pass differentiation is often required in processing various biological or biomechanical data. However, both the nature of biological signals and the use of micro-or minicomputers in such applications imply the need for simple, low-order, and fast differentiation methods, rather than sophisticated high-order algorithms. Responding to this need, we investigate here the low-pass first- and second-order digital differentiation from both theoretical and practical points of view, in order to achieve good and simple algorithms. In contrast with most of the research works previously done in this field, whose main aim was to achieve better accuracy even in the cost of using quite high-order algorithms, we restrict ourselves in this study only to low orders, being interested not only in the accuracy achieved, but also in the simplicity of the algorithm. After discussing the theoretical considerations concerning our optimum low-pass differentiation filters, we present our simple low-order filters and show them to be not only very convenient for use, but also almost optimum.
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paper_title: Weakly supervised pain localization using multiple instance learning

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Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target expression for a given frame is unknown, and (2) the time point and the duration of the pain expression event(s) in each video are unknown. To address these issues we propose a novel framework (referred to as MS-MIL) where each sequence is represented as a bag containing multiple segments, and multiple instance learning (MIL) is employed to handle this weakly labeled data in the form of sequence level ground-truth. These segments are generated via multiple clustering of a sequence or running a multi-scale temporal scanning window, and are represented using a state-of-the-art Bag of Words (BoW) representation. This work extends the idea of detecting facial expressions through `concept frames' to `concept segments' and argues through extensive experiments that algorithms like MIL are needed to reap the benefits of such representation. The key advantages of our approach are: (1) joint detection and localization of painful frames using only sequence-level ground-truth, (2) incorporation of temporal dynamics by representing the data not as individual frames but as segments, and (3) extraction of multiple segments, which is well suited to signals with uncertain temporal location and duration in the video. Experiments on UNBC-McMaster Shoulder Pain dataset highlight the effectiveness of our approach by achieving promising results on the problem of pain detection in videos.
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paper_title: A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features

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An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems
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paper_title: Analysis of Lumbar Spine and Hip Motion During Forward Bending in Subjects With and Without a History of Low Back Pain

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Study Design. This study analyzed two groups of subjects during forward bending. Group 1 (n = 20) contained subjects with a history of low back pain and Group 2 (n = 21) included subjects without a history of low back pain. Objective. The purposes of this study were to establish the amount and pattern of lumbar spine and hip motion during forward bending, and determine differences in motion in subjects with and without a history of low back pain. Summary of Background Data. Reported values for lumbar spine motion during forward bending vary from 23.9° to 60° and hip motion during forward bending ranges from 26° to 66°. There has been no direct study of both lumbar spine and hip motion during forward bending in subjects with and without a history of low back pain to establish differences in total amounts or pattern of lumbar spine and hip motion during forward bending. Methods. A three-dimensional optoelectric motion analysis system was used to measure the amount and velocity of lumbar spine and hip motion during forward bending. Each subject performed three trials of forward bending that were averaged and used for statistical analysis. Hamstring flexibility was also assessed by two clinical tests, the passive straight leg raising and active knee extension tests. Results. Mean total forward bending for all subjects was 111° : 41.6° from the lumbar spine and 69.4° from the hips. There were no group differences for total amounts of lumbar spine and hip motion or velocity during forward bending. The pattern of motion was described by calculating lumbar-to-hip flexion ratios for early (0-30°), middle (30-60°), and late (60-90°) forward bending. For all subjects, mean lumbar-to-hip ratios for early, middle, and late forward bending were 1.9, 0.9, and 0.4, respectively. Therefore, the lumbar spine had a greater contribution to early forward bending, the lumbar spine and hips contributed almost equally to middle forward bending, and the hips had a greater contribution to late forward bending. A ttest revealed a difference between groups for the pattern of motion. Group 1 tended to move more at their lumbar spine during early forward bending and had a significantly lower lumbar-to-hip flexion ratio during middle forward bending (P < 0.01). Hamstring flexibility was strongly correlated to motion in subjects with a history of low back pain, but not in healthy subjects. Conclusions. The results provide quantitative data to guide clinical assessment of forward bending motion. Results also suggest that although people with a history of low back pain have amounts of lumbar spine and hip motion during forward bending similar to those of healthy subjects, the pattern of motion is different. It may be desirable to teach patients with a history of low back pain to use more hip motion during early forward bending, and hamstring stretching may be helpful for encouraging earlier hip motion.
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paper_title: Understanding Caregiver Judgments of Infant Pain: Contrasts of Parents, Nurses and Pediatricians

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BACKGROUND: Research suggests that caregivers’ beliefs pertaining to infant pain and which infant pain cues are perceived to be important play an integral role in pediatric pain assessment and management.
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paper_title: Automated detection of pain from facial expressions: a rule-based approach using AAM

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In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for ::: critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to ::: detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos ::: captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action ::: Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific ::: Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient ::: individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is ::: better suited than the more commonly used classifier-based methods for application to the cancer patient videos in ::: which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points ::: that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to ::: face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units ::: in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained ::: FACS coders who independently reviewed and scored the action units in the cancer patient videos.
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paper_title: The role of spatial frequency information in the recognition of facial expressions of pain.

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Abstract Being able to detect pain from facial expressions is critical for pain communication. Alongside identifying the specific facial codes used in pain recognition, there are other types of more basic perceptual features, such as spatial frequency (SF) which refers to the amount of detail in a visual display. Low-SF carries coarse information, which can be seen from a distance, and high-SF carries fine-detailed information that can only be perceived when viewed close up. Since this type of basic information has not been considered in the recognition of pain we therefore investigated the role of low- and high-SF information in the decoding of facial expressions of pain. Sixty-four pain free adults completed two independent tasks: a multiple expression identification task of pain and core emotional expressions, and a dual expression “either-or” task (pain vs. fear, pain vs. happiness). While both low-SF and high-SF information makes the recognition of pain expressions possible, low-SF information seemed to play a more prominent role. This general low-SF bias would seem an advantageous way of potential threat detection, as facial displays will be degraded if viewed from a distance or in peripheral vision. One exception was found, however, in the “pain-fear” task, where responses were not affected by SF type. Together this not only indicates a flexible role for SF information that depends on task parameters (goal context), but suggests that in challenging visual conditions, we perceive an overall affective quality of pain expressions rather than detailed facial features. Key Words: pain; facial expression; recognition; spatial frequency
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paper_title: Facial Expression Analysis for Estimating Pain in Clinical Settings

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Pain assessment is vital for effective pain management in clinical settings. It is generally obtained via patient's self-report or observer's assessment. Both of these approaches suffer from several drawbacks such as unavailability of self-report, idiosyncratic use and observer bias. This work aims at developing automated machine learning based approaches for estimating pain in clinical settings. We propose to use facial expression information to accomplish current goals since previous studies have demonstrated consistency between facial behavior and experienced pain. Moreover, with recent advances in computer vision it is possible to design algorithms for identifying spontaneous expressions such as pain in more naturalistic conditions. Our focus is towards designing robust computer vision models for estimating pain in videos containing patient's facial behavior. In this regard we discuss different research problem, technical approaches and challenges that needs to be addressed. In this work we particularly highlight the problem of predicting self-report measures of pain intensity since this problem is not only more challenging but also received less attention. We also discuss our efforts towards collecting an in-situ pediatric pain dataset for validating these approaches. We conclude the paper by presenting some results on both UNBC Mc-Master Pain dataset and pediatric pain dataset.
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paper_title: Pupillary responses to emotionally provocative stimuli

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This paper investigated in two experiments pupillary responses to emotionally provocative sound stimuli. In experiment one, 30 subjects' pupillary responses were measured while listening to 10 negatively and 10 positively arousing sounds, and 10 emotionally neutral sounds. In addition, the subjects rated their subjective experiences to these stimuli. The results showed that the pupil size was significantly larger after highly arousing positive and negative stimuli than after neutral stimuli with medium arousal. In experiment two, the contents of the stimuli were more controlled than in experiment one. 22 subjects' pupillary responses were measured while listening to four negatively and four positively arousing sounds, and four emotionally neutral sounds. The results showed that the pupil size was significantly larger during negative highly arousing stimuli than during moderately arousing positive stimuli. The pupil size was also significantly larger after highly arousing negative stimuli than after moderately arousing neutral and positive stimuli. The results of the two experiments suggest that pupil size discriminates during and after different kinds of emotional stimuli. Thus, the measurement of pupil size variation my be a potentially useful computer input signal, for example, for affective computing.
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paper_title: An associate memory model of facial expressions and its application in facial expression recognition of patients on bed

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In this paper, we proposed a new method of monitoring patients on bed by utilizing the facial expression analyses and recognition. In this proposed method, the optical flows of the corresponding image sequence, which appeard the facial actions, were computed. Based on the optical flow projection histogram on and coordinate axes in the mouth region and the eye region, the facial action features were extracted. By utilizing the facial action feature rate of occurrence on the happiness, easiness, uneasiness, disgust, suffering and surprise, the associate memory model of facial expressions is composed to recognize the facial expressions. The classification performance of our associate memory model is demonstrated by using a training set and a testing set from the facial expression database made for our research project.
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paper_title: Measurement of Neonatal Responses to Painful Stimuli: A Research Review

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The measurement of neonatal responses to painful stimuli remains a significant clinical problem. Although numerous measures have been evaluated, instruments that are valid, reliable, and clinically feasible are not yet available. The purpose of this paper is to critique the studies that have been done using biochemical, physiological, and behavioral measures to evaluate neonatal responses to painful stimuli. Specific issues regarding measurement in premature and critically ill neonates are emphasized. The intent of this review and critique of the literature is to stimulate additional research into the assessment of neonatal pain.
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paper_title: Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation

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Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.
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paper_title: Behavioral pain indicators in people with intellectual disabilities: a systematic review.

paper_content:

UNLABELLED ::: People with intellectual disabilities (IDs) have a higher risk of painful medical conditions. Partly because of the impaired ability to communicate about it, pain is often undertreated. To strengthen pain assessment in this population, we conducted a systematic review to identify behavioral pain indicators in people with IDs by using Embase, PubMed, PsycINFO, CINAHL, and Cochrane. Inclusion criteria were 1) scientific papers; 2) published in the last 20 years, that is, 1992 to 2012; 3) written in English, 4) using human subjects, 5) intellectual disabilities, 6) pain, 7) behavior, and 8) an association between observable behavior and pain experience. From 527 publications, 27 studies were included. Pain was acute in 14 studies, chronic in 2 studies, both acute and chronic in 2 studies, and unspecified in 9 studies. Methodological quality was assessed with the Mixed Methods Appraisal Tool. Of the 14 categories with behavioral pain indicators, motor activity, facial activity, social-emotional indicators, and nonverbal vocal expression were the most frequently reported. Most of the behavioral pain indicators are reported in more than 1 study and form a possible clinical relevant set of indicators for pain in people with IDs. Determination of a behavioral pattern specific for pain, however, remains a challenge for future research. ::: ::: ::: PERSPECTIVE ::: This review focuses on categories of behavior indicators related to pain in people with IDs. The quality of evidence is critically discussed per category. This set of indicators could potentially help clinicians to recognize pain in this population, especially when unique individual pain responses are also identified.
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paper_title: Development of Translational Methods in Spectral Analysis of Human Infant Crying and Rat Pup Ultrasonic Vocalizations for Early Neurobehavioral Assessment

paper_content:

The purpose of this article is to describe translational methods by which analyses of crying in young human infants and ultrasonic vocalizations of rat pups can be used to assess effects of adverse prenatal conditions on early neurobehavioral integrity. Whereas the long history of analyses of human infant crying has developed a rich set of measures that are sensitive to neurobehavioral insult, analyses of rat pup vocalizations allow for strict controls over the prenatal conditions that may adversely affect neurobehavioral development. The development of translational methods will allow for findings in respective fields of inquiry to inform one another. To this end, we present an enhanced taxonomy of a novel set of common measures of crying in both human infants and rat pups based on the conceptual framework that infant crying is a graded and dynamic acoustic signal. This set includes latency to vocalization onset, duration and repetition rate of expiratory components, duration of inter-vocalization-intervals and spectral features of the sound, including the frequency and amplitude of the fundamental and dominant frequencies. We also present a new set of classifications of rat pup USV waveforms that include qualitative shifts in fundamental frequency, similar to the presence of qualitative shifts in fundamental frequency that have previously been related to insults to neurobehavioral integrity in human infants. Challenges to the development of translational analyses, including the use of different terminologies, methods of recording and spectral analyses are discussed, as well as descriptions of automated processes, software solutions and pitfalls.
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paper_title: Survey on speech emotion recognition: Features, classification schemes, and databases

paper_content:

Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable features for speech representation. The second issue is the design of an appropriate classification scheme and the third issue is the proper preparation of an emotional speech database for evaluating system performance. Conclusions about the performance and limitations of current speech emotion recognition systems are discussed in the last section of this survey. This section also suggests possible ways of improving speech emotion recognition systems.
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paper_title: Forefoot deformity, pain, and mobility in rheumatoid and nonarthritic subjects.

paper_content:

OBJECTIVE ::: To evaluate how painful metatarsal arthritis affects foot and ankle mechanics and mobility. ::: ::: ::: METHODS ::: We studied 16 symptomatic forefeet in 10 patients with rheumatoid arthritis (RA) and compared them with 14 asymptomatic forefeet in 7 nonarthritic subjects. RA limbs with significant disease at other locations were excluded. We measured pain and deformity of the foot using a visual analog scale and a modified articular index. A video based 3 dimensional gait analysis system and force platform were used to collect data on subjects walking barefoot at a self-selected pace according to an established protocol. Mobility level was quantified using the Sickness Impact Profile (SIP) ambulation subscale. ::: ::: ::: RESULTS ::: We observed considerable pain and deformity of the forefeet of RA subjects. During gait, motion and force measures revealed that RA subjects significantly (p < 0.005) delayed and reduced forefoot loading, which minimized use of the foot as a rigid level for push off. As a result, stride lengths were shorter and gait was slower compared to nonarthritic subjects. SIP scores revealed that these changes in gait resulted in moderate disability in RA subjects (p=0.05). ::: ::: ::: CONCLUSION ::: Impairments of the forefoot due to RA include pain and deformity, which produce characteristic stance phase abnormalities in foot function, a slow walking speed, and moderate disability.
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paper_title: Pain Expression Recognition Based on SLPP and MKSVM

paper_content:

In this paper, a novel approach is proposed for recognizing pain expression. First of all, supervised locality preserving projections (SLPP) is adopted for extracting feature of pain expression, which can solve the problem that LPP ignores the within-class local structure using adopting prior class label information, and then multiple kernels support vector machines (MKSVM) is employed for recognizing pain expression, Compared to SVM, which can improve the interpretability of decision function and classifier performance. Experimental results are shown to demonstrate the effectiveness of the proposed method.
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paper_title: Acoustical Analysis of Pain Cries' in Neonates: Fundamental Frequency

paper_content:

crying comprises a rhythmic alternation of cry sounds (utterances) and inspiration. A method for knowing the reason of baby's cry is proposed using frequency analysis of the recorded voice. The analysis has been done by using Welch's method and performing Fast Fourier Transform (FFT). The frequency analysis helps in finding the different tendencies of the Fourier spectrum for different causes. To find the cry type, fundamental frequency is considered. The acoustical analysis of cry, it is found that the fundamental frequency f0 for cry due to pain is found to be around 400Hz. The male cry f0 is higher than the female cry mean f0.
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paper_title: Automatic Decoding of Facial Movements Reveals Deceptive Pain Expressions

paper_content:

Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55%. However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling.
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paper_title: Facing others in pain: the effects of empathy

paper_content:

An electric fishing float with flash indicators comprising of an elongated tubular fishing float case containing a cell used as a power source, a light source, such as a plurality of light emitting diodes adjacent a closed top end of the case and a piston-like switch member mounted to reciprocate in the fishing float case between a first position to turn on the light source and a second position to turn off the light source in response to biting by a fish.
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paper_title: Acoustic Analyses of Developmental Changes and Emotional Expression in the Preverbal Vocalizations of Infants

paper_content:

Abstract The nonverbal vocal utterances of seven normally hearing infants were studied within their first year of life with respect to age- and emotion-related changes. Supported by a multiparametric acoustic analysis it was possible to distinguish one inspiratory and eleven expiratory call types. Most of the call types appeared within the first two months; some emerged in the majority of infants not until the 5th (“laugh”) or 7th month (“babble”). Age-related changes in acoustic structure were found in only 4 call types (“discomfort cry,” “short discomfort cry,” “wail,” “moan”). The acoustic changes were characterized mainly by an increase in harmonic-to-noise ratio and homogeneity of the call, a decrease in frequency range and a downward shift of acoustic energy from higher to lower frequencies. Emotion-related differences were found in the acoustic structure of single call types as well as in the frequency of occurrence of different call types. A change from positive to negative emotional state was accompanied by an increase in call duration, frequency range, and peak frequency (frequency with the highest amplitude within the power spectrum). Negative emotions, in addition, were characterized by a significantly higher rate of “crying,” “hic” and “ingressive vocalizations” than positive emotions, while positive emotions showed a significantly higher rate of “babble,” “laugh,” and “raspberry.”
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paper_title: AUTOMATIC CLASSIFICATION OF INFANTS' CRY

paper_content:

The cry sound produced by an infant is the result of his/her physical and psychological condition and/or internal/external stimuli. It has been shown that different cry origins, such as pain, hunger, insult and fear, exhibit different cry patterns. Special characteristics of newborn pain cries were identified in various pathological states such as brain damage, cleft palate, hydrocephalus and sudden infant death syndrome (SIDS). Interestingly, there is a disease known to be characterized especially by its unusual cry and is named accordingly: Cri-du-chat ("Cry of the cat") syndrome. Analysis of the cry as a non-invasive diagnostic tool is most important when dealing with diseases in which early diagnosis is not available (such as SIDS). Infants’ cry analysis, reported in the literature, was performed mainly by human analysis of cry spectrographs. The method is strongly dependent on subjective evaluation and is not suitable for clinical use. Thus, there is a need to develop
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paper_title: Atypical Cry Acoustics in 6-Month-Old Infants at Risk for Autism Spectrum Disorder

paper_content:

This study tested whether infants at risk for autism spectrum disorders (autism or ASDs) show differences in vocal production in early infancy by examining whether infant siblings of children with autism showed alterations in acoustic properties of infant cries at 6 months of age. A common focus of early infant studies of autism has been on aspects of social responses and relatedness in early development. Findings have revealed that disruptions in social behaviors by 12 months, including poor response to name, atypical eye contact, and reduced social interest and social smiling, distinguish at-risk babies who are later diagnosed with autism [Zwaigenbaum et al., 2005]. Some studies have found alterations in social responses during the face-to-face/still-face procedure as early as 4–6 months of age, including fewer smiles directed at mothers [Cassel et al., 2007], diminished gaze to eyes relative to mouth [Merin, Young, Ozonoff, & Rogers, 2007], and less interactional synchrony between babies and mothers [Yirmiya et al., 2006]. However, in general, it has been difficult to identify robust deficits in social behaviors associated with autism in early infancy [Ozonoff et al., 2010]. However, other atypicalities seen in childhood autism may also be investigated in infancy. For example, atypical vocal production has been studied in toddlers and young children with autism [Oller et al., 2010; Schoen, Paul, & Chawarska, 2011; Sheinkopf, Mundy, Oller, & Steffens, 2000; Woods & Wetherby, 2003]. These studies have found that autism is associated with atypical vocal quality and that such vocal parameters may help to discriminate children with or at risk for a diagnosis. Such findings lead to the prediction that differences in infant vocal production may be evident in babies at risk for or later diagnosed with autism. ::: ::: Recently, Paul, Fuerst, Ramsay, Chawarska, and Klin, [2010] reported that infants at risk for autism produce lower rates of canonical syllables by 9 months but not at 6 months of age. Because this capacity to produce well-formed babbles is just emerging in early infancy, age-specific findings may reflect a floor effect for the sensitivity of phonological structure to detect differences between risk groups at young ages. Nonsegmental aspects of vocal production may be more sensitive to differences associated with autism in early infancy. Infant cries are one type of nonsegmental production that can be observed from the earliest days of life. Cry acoustics have been studied in other clinical and at-risk populations, and may be disrupted in infants at risk for autism. ::: ::: Esposito and Venuti [2009, 2010] have reported differences in observer ratings of cry at 12 months of age in later-diagnosed babies from home videotapes. Observers rated the cries of infants later diagnosed with autism as having more atypical features and fewer prototypical cry patterns than babies with typical development and those with developmental delays [Esposito & Venuti, 2009]. These researchers also reported higher fundamental frequency (F0) of cries in later-diagnosed infants vs. typically developing infants by 12 months of age [Esposito & Venuti, 2010]. Differences in infant cry acoustics have not yet been found in relation to autism earlier in development. ::: ::: The production of cry is the result of air being forced through the vocal tract and over the larynx. Vibrations of the vocal folds result in the sound that is perceived by humans as crying. This sound can be described with a variety of parameters, including the loudness or amplitude of the cry, the timing of cry onset, and length of interutterance intervals. Other aspects of cries can be represented acoustically by describing features such as F0, voicing, and formant frequencies. F0 is the base frequency of a cry and is perceived as pitch. Voicing, also called phonation, describes cry sounds resulting from harmonic vibration of the vocal folds. Formants (e.g. F1, F2, etc.) describe additional resonant frequencies in the sound spectrum, and in speech recognition, work can be used to describe the configuration of the vocal tract. ::: ::: There is a long history of research on infant crying in populations considered at risk for developmental differences, as well as those with identified conditions [Karelitz & Fisichelli, 1962; Prechtl, Theorell, Gramsbergen, & Lind, 1969; Wasz-Hockert, Lind, Vourenkoski, Partanen, & Valanne, 1968]. This research includes more recent work describing acoustic features of infant crying in relation to risk for poor developmental outcomes [Gibbins et al., 2008; Goberman & Robb, 1999; Quick, Robb, & Woodward, 2009; Zeskind, Platzman, Coles, & Schuetze, 1996]. These results include differences in F0 and, to some degree, other measures of vocal tract configuration (i.e. formants) in infants with perinatal risks or medical complications such as hyperbilirubinemia, prenatal substance exposure, lead exposure, or frank evidence of brain damage [see LaGasse, Neal, & Lester, 2005]. Thus, cry characteristics are an indication of neurobehavioral risk. However, crying can also be understood in the context of a biosocial model, where infant crying is seen as both a physiologic event and part of a dyadic communication system between baby and caregivers [Lester, 1984]. In relation to autism, cry acoustics may reflect underlying neurobehavioral differences associated with the disorder and may also reflect differences in the development of the parent-infant dyad as well as being predictive of long-term developmental outcome [Lester, 1987]. ::: ::: Research in clinical populations has tended to focus on differences in average and maximum values of F0, range or variability of F0 across cry utterances, and other measures used to describe the acoustic characteristics of cries. However, other research, especially work in typical populations, has examined features that can be described as reflecting prosodic aspects of cry sounds. Work on cry prosody focuses on aspects such as the contour of cry pitch [i.e. nonlinear changes in F0 across an utterance; see, for example, Mampe, Friederici, Christophe, & Wermke, 2009 and Wermke, Mende, Manfredi, & Bruscaglioni, 2002]. ::: ::: Studies of prosodic features of cry may have value in the study of infants at risk for autism given the overall delays in language, and the differences in prosodic speech characteristics are often characteristics of children and adults with autism [Tager-Flusberg, 2005]. However, in order to be consistent with prior research linking cry characteristics with perinatal risks and poor developmental outcomes, the current study examines measures of cry utterance characteristics such as the average value and variability in F0, as well as measures of voicing in relation to risk for autism. This level of analysis is consistent with prior research on vocal characteristics of young children with autism. For example, research has found that young children with autism produce vocalizations with less clear emotional signals [Ricks & Wing, 1975], higher rates of atypical vocalizations [Wetherby et al., 2004; Wetherby, Yonclas, & Bryan, 1989], and with atypical voicing described as highly tense or high-pitched [Sheinkopf et al., 2000]. In an acoustic framework, these vocalizations could be characterized as having high overall F0 and unusual phonation or voicing. ::: ::: In the study reported in this paper, our aim was to investigate acoustic features of cries that may be linked to arousal, regulation, and neurobehavioral integrity. As such, overall descriptions of features such as F0 and phonation (voicing) were the focus of this study. We report initial findings of an investigation of cry production in a group of infants at risk for autism and a low-risk (LR) comparison sample. Prior findings from young children with autism and the limited number of observations of infants later diagnosed with autism led to the prediction that autism-related cry atypicalities will be found in the acoustic features of F0 and phonation, representing the degree to which cries are voiced. Differences in the amplitude or force of cry may also be present but are more difficult to operationalize because of measurement and recording difficulties.
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paper_title: Improving pain recognition through better utilisation of temporal information

paper_content:

Automatically recognizing pain from video is a very useful application ::: as it has the potential to alert carers to patients that are ::: in discomfort who would otherwise not be able to communicate ::: such emotion (i.e young children, patients in postoperative ::: care etc.). In previous work [1], a “pain-no pain” system was ::: developed which used an AAM-SVM approach to good effect. ::: However, as with any task involving a large amount of video ::: data, there are memory constraints that need to be adhered to ::: and in the previous work this was compressing the temporal ::: signal using K-means clustering in the training phase. In visual ::: speech recognition, it is well known that the dynamics of the ::: signal play a vital role in recognition. As pain recognition is ::: very similar to the task of visual speech recognition (i.e. recognising ::: visual facial actions), it is our belief that compressing ::: the temporal signal reduces the likelihood of accurately recognising ::: pain. In this paper, we show that by compressing the ::: spatial signal instead of the temporal signal, we achieve better ::: pain recognition. Our results show the importance of the temporal ::: signal in recognizing pain, however, we do highlight some ::: problems associated with doing this due to the randomness of a ::: patient's facial actions.
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paper_title: Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

paper_content:

More than a decade has passed since research on automatic recognition of emotion from speech has become a new field of research in line with its 'big brothers' speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In a first part, we address the basic phenomenon reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then shift to automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the first comparative challenge on emotion recognition from speech - the INTERSPEECH 2009 Emotion Challenge, organised by (part of) the authors, including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the actual learnt lessons before we finally address the ever-lasting problems and future promising attempts.
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paper_title: Pain in the preterm neonate: behavioural and physiological indices

paper_content:

The impact of invasive procedures on preterm neonates has received little systematic attention. We examined facial activity, body movements, and physiological measures in 56 preterm and full-term newborns in response to heel lancing, along with comparison preparatory and recovery intervals. The measures were recorded in special care and full-term nurseries during routine blood sampling. Data analyses indicated that in all measurement categories reactions of greatest magnitude were to the lancing procedure. Neonates with gestational ages as short as 25-27 weeks displayed physiological responsivity to the heel lance, but only in the heart rate measure did this vary with gestational age. Bodily activity was diminished in preterm neonates in general, relative to full-term newborns. Facial activity increased with the gestational age of the infant. Specificity of the response to the heel lance was greatest on the facial activity measure. Identification of pain requires attention to gestational age in the preterm neonate.
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paper_title: Pain expression in neonates: facial action and cry

paper_content:

Pain expression in neonates instigated by heel-lance for blood sampling purposes was systematically described using measures of facial expression and cry and compared across sleep/waking states and sex. From gate-control theory it was hypothesized that pain behavior would vary with the ongoing functional state of the infant, rather than solely reflecting tissue insult. Awake-alert but inactive infants responded with the most facial activity, consistent with current views that infants in this state are most receptive to environmental stimulation. Infants in quiet sleep showed the least facial reaction and the longest latency to cry. Fundamental frequency of cry was not related to sleep/waking state. This suggested that findings from the cry literature on qualities of pain cry as a reflection of nervous system 'stress', in unwell newborns, do not generalize directly to healthy infants as a function of state. Sex differences were apparent in speed of response, with boys showing shorter time to cry and to display facial action following heel-lance. The findings of facial action variation across sleep/waking state were interpreted as indicating that the biological and behavioral context of pain events affects behavioral expression, even at the earliest time developmentally, before the opportunity for learned response patterns occurs. Issues raised by the study include the importance of using measurement techniques which are independent of preconceived categories of affective response.
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paper_title: Gender is a confounding factor in pain trials: women report more pain than men after arthroscopic surgery

paper_content:

Abstract A gender difference in the incidence of acute pain may be a confounder in analgesic trials. We have tested the hypothesis that the incidence of acute pain after knee arthroscopic procedures is greater in women than men. We performed three RCTs on intra-articular analgesics in which no postoperative analgesia was given until the need for such treatment was documented by scoring moderate-to-severe pain on a verbal rating scale (VRS 0–4; n =219), and a 0–100 mm visual analogue pain scale (VAS) within 2 h postoperatively. All trials were performed with an intra-articular catheter technique. The design allowed us to study the natural course of pain after arthroscopic surgery until analgesia was required. Women reported more pain of at least moderate intensity than men (84 vs 57%; P
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paper_title: Agitation and pain assessment using digital imaging

paper_content:

Pain assessment in patients who are unable to verbally communicate with medical staff is a challenging problem in patient critical care. The fundamental limitations in sedation and pain assessment in the intensive care unit (ICU) stem from subjective assessment criteria, rather than quantifiable, measurable data for ICU sedation and analgesia. This often results in poor quality and inconsistent treatment of patient agitation and pain from nurse to nurse. Recent advancements in pattern recognition techniques using a relevance vector machine algorithm can assist medical staff in assessing sedation and pain by constantly monitoring the patient and providing the clinician with quantifiable data for ICU sedation. In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners.
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paper_title: A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states

paper_content:

This paper focuses on the use of image-based techniques for classifying pain states, in particular we compare several texture descriptors based on Local Binary Patterns (LBP), and we proposed some novel solutions based on the combination of new texture descriptors: the Elongated Ternary Pattern (ELTP) and the Elongated Binary Pattern (ELBP). ELTP is the best performing descriptor in our experiments. The ELBP descriptor combines characteristics of the Local Ternary Pattern (LTP) and ELTP. These two variants of the standard LBP are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. The resulting extracted features are used to train a support vector machine classifier. Extensive experiments are conducted using the Infant COPE database of neonatal facial images. Our results show that a local approach based on the ELTP feature extractor produces a reliable system for classifying pain states.
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paper_title: Introduction to Neonatal Facial Pain Detection Using Common and Advanced Face Classification Techniques

paper_content:

Assessing pain in neonates is a challenging problem. Neonates cannot describe their pain experiences but must rely exclusively on the judgments of others. Studies demonstrate, however, that proper diagnosis of pain is impeded by observer bias. It has therefore been recommended that neonatal pain assessment instruments include evaluations that have bypassed an observer. In this article, we describe the Infant COPE project and our work using face classification to detect pain in a neonate’s facial displays. We begin by providing an introduction to face classification that includes an outline of some common and advanced algorithms. We then describe a small database we designed specifically to investigate classifier performance in this problem domain. This is followed by a summary of the experiments we have performed to date, including some preliminary results of current work. We believe these results indicate that the application of face classification to the problem of neonatal pain assessment is a promising area of investigation.
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paper_title: Pain Expression Recognition Based on SLPP and MKSVM

paper_content:

In this paper, a novel approach is proposed for recognizing pain expression. First of all, supervised locality preserving projections (SLPP) is adopted for extracting feature of pain expression, which can solve the problem that LPP ignores the within-class local structure using adopting prior class label information, and then multiple kernels support vector machines (MKSVM) is employed for recognizing pain expression, Compared to SVM, which can improve the interpretability of decision function and classifier performance. Experimental results are shown to demonstrate the effectiveness of the proposed method.
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paper_title: Facing others in pain: the effects of empathy

paper_content:

An electric fishing float with flash indicators comprising of an elongated tubular fishing float case containing a cell used as a power source, a light source, such as a plurality of light emitting diodes adjacent a closed top end of the case and a piston-like switch member mounted to reciprocate in the fishing float case between a first position to turn on the light source and a second position to turn off the light source in response to biting by a fish.
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paper_title: Eigenimage Based Pain Expression Recognition

paper_content:

In this paper, we present a robust approach for pain expression recognition from video sequences. An automatic face detector is employed which uses skin color modeling to detect human face in the video sequence. The pain affected portions of the face are obtained by using a mask image. The obtained face images are then projected onto a feature space, defined by Eigenfaces, to produce the biometric template. Pain recognition is performed by projecting a new image onto the feature spaces spanned by the Eigenfaces and then classifying the painful face by comparing its position in the feature spaces with the positions of known individuals.
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paper_title: Pain Expression Recognition Based on pLSA Model

paper_content:

We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: "no pain," "slight pain," "moderate pain," and " severe pain." First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness.
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paper_title: An associate memory model of facial expressions and its application in facial expression recognition of patients on bed

paper_content:

In this paper, we proposed a new method of monitoring patients on bed by utilizing the facial expression analyses and recognition. In this proposed method, the optical flows of the corresponding image sequence, which appeard the facial actions, were computed. Based on the optical flow projection histogram on and coordinate axes in the mouth region and the eye region, the facial action features were extracted. By utilizing the facial action feature rate of occurrence on the happiness, easiness, uneasiness, disgust, suffering and surprise, the associate memory model of facial expressions is composed to recognize the facial expressions. The classification performance of our associate memory model is demonstrated by using a training set and a testing set from the facial expression database made for our research project.
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paper_title: Determining Optical Flow

paper_content:

Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantified rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
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paper_title: Active appearance models

paper_content:

We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
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paper_title: Improving pain recognition through better utilisation of temporal information

paper_content:

Automatically recognizing pain from video is a very useful application ::: as it has the potential to alert carers to patients that are ::: in discomfort who would otherwise not be able to communicate ::: such emotion (i.e young children, patients in postoperative ::: care etc.). In previous work [1], a “pain-no pain” system was ::: developed which used an AAM-SVM approach to good effect. ::: However, as with any task involving a large amount of video ::: data, there are memory constraints that need to be adhered to ::: and in the previous work this was compressing the temporal ::: signal using K-means clustering in the training phase. In visual ::: speech recognition, it is well known that the dynamics of the ::: signal play a vital role in recognition. As pain recognition is ::: very similar to the task of visual speech recognition (i.e. recognising ::: visual facial actions), it is our belief that compressing ::: the temporal signal reduces the likelihood of accurately recognising ::: pain. In this paper, we show that by compressing the ::: spatial signal instead of the temporal signal, we achieve better ::: pain recognition. Our results show the importance of the temporal ::: signal in recognizing pain, however, we do highlight some ::: problems associated with doing this due to the randomness of a ::: patient's facial actions.
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paper_title: Video-based facial discomfort analysis for infants

paper_content:

Prematurely born infants receive special care in the Neonatal Intensive Care Unit (NICU), where various physiological parameters, such as heart rate, oxygen saturation and temperature are continuously monitored. However, there is no system for monitoring and interpreting their facial expressions, the most prominent discomfort indicator. In this paper, we present an experimental video monitoring system for automatic discomfort detection in infants’ faces based on the analysis of their facial expressions. The proposed system uses an Active Appearance Model (AAM) to robustly track both the global motion of the newborn’s face, as well as its inner features. The system detects discomfort by employing the AAM representations of the face on a frame-by-frame basis, using a Support Vector Machine (SVM) classifier. Three contributions increase the performance of the system. First, we extract several histogram-based texture descriptors to improve the AAM appearance representations. Second, we fuse the outputs of various individual SVM classifiers, which are trained on features with complementary qualities. Third, we improve the temporal behavior and stability of the discomfort detection by applying an averaging filter to the classification outputs. Additionally, for a higher robustness, we explore the effect of applying different image pre-processing algorithms for correcting illumination conditions and for image enhancement to evaluate possible detection improvements. The proposed system is evaluated in 15 videos of 8 infants, yielding a 0.98 AUC performance. As a bonus, the system offers monitoring of the infant’s expressions when it is left unattended and it additionally provides objective judgment of discomfort.
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paper_title: Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain

paper_content:

We present initial results from the application of an automated facial expression recognition system to spontaneous facial expressions of pain. In this study, 26 participants were videotaped under three experimental conditions: baseline, posed pain, and real pain. In the real pain condition, subjects experienced cold pressor pain by submerging their arm in ice water. Our goal was to automatically determine which experimental condition was shown in a 60 second clip from a previously unseen subject. We chose a machine learning approach, previously used successfully to categorize basic emotional facial expressions in posed datasets as well as to detect individual facial actions of the Facial Action Coding System (FACS) (Littlewort et al, 2006; Bartlett et al., 2006). For this study, we trained 20 Action Unit (AU) classifiers on over 5000 images selected from a combination of posed and spontaneous facial expressions. The output of the system was a real valued number indicating the distance to the separating hyperplane for each classifier. Applying this system to the pain video data produced a 20 channel output stream, consisting of one real value for each learned AU, for each frame of the video. This data was passed to a second layer of classifiers to predict the difference between baseline and pained faces, and the difference between expressions of real pain and fake pain. Naive human subjects tested on the same videos were at chance for differentiating faked from real pain, obtaining only 52% accuracy. The automated system was successfully able to differentiate faked from real pain. In an analysis of 26 subjects, the system obtained 72% correct for subject independent discrimination of real versus fake pain on a 2-alternative forced choice. Moreover, the most discriminative facial action in the automated system output was AU 4 (brow lower), which all was consistent with findings using human expert FACS codes.
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paper_title: Variable-state latent conditional random fields for facial expression recognition and action unit detection

paper_content:

Automatic recognition of facial expressions of emotions, and detection of facial action units (AUs), from videos depends critically on modeling of their dynamics. These dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion/AUs, the appearance of which vary considerably among subjects, making the recognition/detection task very challenging. While state-of-the-art Latent Conditional Random Fields (LCRF) allow one to efficiently encode these dynamics via modeling of structural information (e.g., temporal consistency and ordinal constraints), their latent states are restricted to either unordered (nominal) or fully ordered (ordinal). However, such an approach is often too restrictive since, for instance, in the case of AU detection, the sequences of an active AU may better be described using ordinal latent states (corresponding to the AU intensity levels), while the sequences of this AU not being active may better be described using unordered (nominal) latent states. To this end, we propose the Variable-state LCRF model that automatically selects the optimal latent states (nominal or ordinal) for each sequence from each target class. This unsupervised adaptation of the model to individual sequence or subject contexts opens the possibility for improved model fitting and, subsequently, enhanced predictive performance. Our experiments on four public expression databases (CK+, AFEW, MMI and GEMEP-FERA) show that the proposed model consistently outperforms the state-of-the-art methods for both facial expression recognition and action unit detection from image sequences.
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paper_title: Fully Automatic Facial Action Recognition in Spontaneous Behavior

paper_content:

We present results on a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and codes each frame with respect to 20 Action units. We present preliminary results on a task of facial action detection in spontaneous expressions during discourse. Support vector machines and AdaBoost classifiers are compared. For both classifiers, the output margin predicts action unit intensity.
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paper_title: Action unit detection using sparse appearance descriptors in space-time video volumes

paper_content:

Recently developed appearance descriptors offer the opportunity for efficient and robust facial expression recognition. In this paper we investigate the merits of the family of local binary pattern descriptors for FACS Action-Unit (AU) detection. We compare Local Binary Patterns (LBP) and Local Phase Quantisation (LPQ) for static AU analysis. To encode facial expression dynamics, we extend the purely spatial representation LPQ to a dynamic texture descriptor which we call Local Phase Quantisation from Three Orthogonal Planes (LPQ-TOP), and compare this with the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). The efficiency of these descriptors is evaluated by a fully automatic AU detection system and tested on posed and spontaneous expression data collected from the MMI and SEMAINE databases. Results show that the systems based on LPQ achieve higher accuracy rate than those using LBP, and that the systems that utilise dynamic appearance descriptors outperform those that use static appearance descriptors. Overall, our proposed LPQ-TOP method outperformed all other tested methods.
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paper_title: Automatic Decoding of Facial Movements Reveals Deceptive Pain Expressions

paper_content:

Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55%. However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling.
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paper_title: The Faces Pain Scale – Revised: toward a common metric in pediatric pain measurement

paper_content:

The Faces Pain Scale (FPS; Bieri et al., Pain 41 (1990) 139) is a self-report measure used to assess the intensity of children's pain. Three studies were carried out to revise the original scale and validate the adapted version. In the first phase, the FPS was revised from its original seven faces to six, while maintaining its desirable psychometric properties, in order to make it compatible in scoring with other self-rating and observational scales which use a common metric (0-5 or 0-10). Using a computer-animated version of the FPS developed by Champion and colleagues (Sydney Animated Facial Expressions Scale), psychophysical methods were applied to identify four faces representing equal intervals between the scale values representing least pain and most pain. In the second phase, children used the new six-face Faces Pain Scale-Revised (FPS-R) to rate the intensity of pain from ear piercing. Its validity is supported by a strong positive correlation (r=0.93, N=76) with a visual analogue scale (VAS) measure in children aged 5-12 years. In the third phase, a clinical sample of pediatric inpatients aged 4-12 years used the FPS-R and a VAS or the colored analogue scale (CAS) to rate pain during hospitalization for surgical and non-surgical painful conditions. The validity of the FPS-R was further supported by strong positive correlations with the VAS (r=0.92, N=45) and the CAS (r=0.84, N=45) in this clinical sample. Most children in all age groups including the youngest were able to use the FPS-R in a manner that was consistent with the other measures. There were no significant differences between the means on the FPS-R and either of the analogue scales. The FPS-R is shown to be appropriate for use in assessment of the intensity of children's acute pain from age 4 or 5 onward. It has the advantage of being suitable for use with the most widely used metric for scoring (0-10), and conforms closely to a linear interval scale.
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paper_title: Judgments of infant pain: the impact of caregiver identity and infant age.

paper_content:

OBJECTIVE ::: To examine whether caregiver judgments of infant pain would vary systematically with different infant caregiver groups and infant age. ::: ::: ::: METHODS ::: A total of 123 caregivers (41 parents, 41 in patient nurses, 41 pediatricians) viewed videotapes of the vigorous behavioral responses of healthy infants (aged 2, 4, 6, 12, and 18 months) to a routine immunization injection and provided ratings of both the affective distress and pain intensity observed. ::: ::: ::: RESULTS ::: A principal components analysis of affective and intensity ratings yielded a weighted pain summary score for each injection event. Older infants were attributed significantly more pain than younger infants, even though the vigor of the behavioral reactions was experimentally controlled across age groups. A profile analysis contrasting observer groups indicated that pediatricians attributed significantly lower levels of pain than parents, while nurses were intermediate to the other groups, not significantly differing from either group. These systematic differences in judgments were consistent across infant age groups. ::: ::: ::: CONCLUSIONS ::: The findings reveal systematic sources of significant variations in observer judgments of infant pain. Despite an absence of differences in the behavioral reactions of the children, both the type of caregiver and their knowledge of the child's age systematically influenced attributions of pain to infants. This work suggests the important role of caregiver role variation and perceived developmental maturity as determinants of infant pain judgments and highlights potential areas of difficulty in controlling the unnecessary suffering of infants.
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paper_title: Estimation of Infants' Cry Fundamental Frequency using a Modified SIFT algorithm

paper_content:

This paper addresses the problem of infants' cry fundamental frequency estimation. The fundamental frequency is estimated using a modified simple inverse filtering tracking (SIFT) algorithm. The performance of the modified SIFT is studied using a real database of infants' cry. It is shown that the algorithm is capable of overcoming the problem of under-estimation and over-estimation of the cry fundamental frequency, with an estimation accuracy of 6.15% and 3.75%, for hyperphonated and phonated cry segments, respectively. Some typical examples of the fundamental frequency contour in typical cases of pathological and healthy cry signals are presented and discussed.
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paper_title: Acoustic analysis of the infant cry: classical and new methods

paper_content:

In the last few decades many papers have written about the analysis of the infant cry. The acoustic analysis has a shorter history than emotional, physiological, etc. investigations. This paper deals with classical and new methods of acoustic analysis of the infant cry. The final goal is to detect hearing disorders according to the crying at the earliest possible moment. Classical acoustic methods were reproduced and compared with solutions, which were not available before. This paper most of all deals with the characteristics of the fundamental frequency of the cry in the time and in the frequency domain.
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paper_title: Emotion Detection From Infant Facial Expressions And Cries

paper_content:

A new system for translating the infant cries from its facial image and cry sounds is presented in this paper. The system is designed to analyze the facial image and sound of the crying infant to derive the reason why the infant is crying. The image and the sound represent the same cry event. The image processing module determines the state of certain facial features, certain combinations of which determine the reason for crying. The sound processing module analyzes the data for the fundamental frequency and the first two formants and uses k-means clustering to determine the reason of the cry. The decisions from the image and sound processing modules are then fused using a decision level fusion system. The overall accuracy of the image and sound processing modules are 64% and 74.2%, respectively, and that of the fused decision is 75.2%.
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paper_title: Variable-state latent conditional random fields for facial expression recognition and action unit detection

paper_content:

Automatic recognition of facial expressions of emotions, and detection of facial action units (AUs), from videos depends critically on modeling of their dynamics. These dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion/AUs, the appearance of which vary considerably among subjects, making the recognition/detection task very challenging. While state-of-the-art Latent Conditional Random Fields (LCRF) allow one to efficiently encode these dynamics via modeling of structural information (e.g., temporal consistency and ordinal constraints), their latent states are restricted to either unordered (nominal) or fully ordered (ordinal). However, such an approach is often too restrictive since, for instance, in the case of AU detection, the sequences of an active AU may better be described using ordinal latent states (corresponding to the AU intensity levels), while the sequences of this AU not being active may better be described using unordered (nominal) latent states. To this end, we propose the Variable-state LCRF model that automatically selects the optimal latent states (nominal or ordinal) for each sequence from each target class. This unsupervised adaptation of the model to individual sequence or subject contexts opens the possibility for improved model fitting and, subsequently, enhanced predictive performance. Our experiments on four public expression databases (CK+, AFEW, MMI and GEMEP-FERA) show that the proposed model consistently outperforms the state-of-the-art methods for both facial expression recognition and action unit detection from image sequences.
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paper_title: A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks

paper_content:

In this paper we present the methodologies and experiments followed for the implementation of a system used for the automatic recognition and classification of patterns of infant cry. We show the different stages through which the system is trained to identify normal and hypo acoustic (deaf) cry. The cry patterns are represented by acoustic features obtained by the Mel-Frequency Cepstrum and Lineal Prediction Coding techniques. For the classification we used a feed-forward neural network. Results from the different methodologies and experiments are shown, as well as the best results obtained up to the moment, which are up to 96.9% of accuracy.
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paper_title: Type-2 fuzzy sets applied to pattern matching for the classification of cries of infants under neurological risk

paper_content:

Crying is an acoustic event that contains information about the functioning of the central nervous system, and the analysis of the infant's crying can be a support in the distinguishing diagnosis in cases like asphyxia and hyperbilirrubinemia. The classification of baby cry has been intended by the use of different types of neural networks and other recognition approaches. In this work we present a pattern classification algorithm based on fuzzy logic Type 2 with which the classification of infant cry is realized. Experiments as well as results are also shown.
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paper_title: A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks

paper_content:

In this paper we present the methodologies and experiments followed for the implementation of a system used for the automatic recognition and classification of patterns of infant cry. We show the different stages through which the system is trained to identify normal and hypo acoustic (deaf) cry. The cry patterns are represented by acoustic features obtained by the Mel-Frequency Cepstrum and Lineal Prediction Coding techniques. For the classification we used a feed-forward neural network. Results from the different methodologies and experiments are shown, as well as the best results obtained up to the moment, which are up to 96.9% of accuracy.
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paper_title: Characterization of infant cries using spectral and prosodic features

paper_content:

In this paper, spectral and prosodic features are explored for recognition of infant cry. Different types of infant cries considered in this work are wet-diaper, hunger and pain. In this work, mel-frequency cepstral coefficients (MFCC) are used to represent the spectral information, and short-time frame energies (STE) and pause duration are used for representing the prosodic information. Support Vector Machines (SVM) are used to capture the discriminative information with respect to above mentioned cries from the spectral and prosodic features. SVM models are developed seperately using spectral and prosodic features. For carrying out these studies, infant cry database collected under Telemedicine project at IIT-KGP has been used. The recognition performance of the developed SVM models using spectral and prosodic features is observed to be 61.11% and 57.41% respectively. In this work, we also examined the recognition performance by combining the spectral and prosodic information at feature and score levels. The recognition performance using feature and score level fusion is observed to be 74.07% and 80.56% respectively.
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paper_title: Movement analysis in infancy may be useful for early diagnosis of autism

paper_content:

All of the 17 autistic children studied in the present paper showed disturbances of movement that with our methods could be detected clearly at the age of 4–6 months, and sometimes even at birth. We used the Eshkol–Wachman Movement Analysis System in combination with still-frame videodisc analysis to study videos obtained from parents of children who had been diagnosed as autistic by conventional methods, usually around 3 years old. The videos showed their behaviors when they were infants, long before they had been diagnosed as autistic. The movement disorders varied from child to child. Disturbances were revealed in the shape of the mouth and in some or all of the milestones of development, including, lying, righting, sitting, crawling, and walking. Our findings support the view that movement disturbances play an intrinsic part in the phenomenon of autism, that they are present at birth, and that they can be used to diagnose the presence of autism in the first few months of life. They indicate the need for the development of methods of therapy to be applied from the first few months of life in autism.
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paper_title: Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

paper_content:

Background ::: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. ::: ::: ::: Methods ::: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. ::: ::: ::: Results ::: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. ::: ::: ::: Conclusion ::: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.
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paper_title: Noxious stimulation in children receiving general anaesthesia evokes an increase in delta frequency brain activity

paper_content:

More than 235,000 children/year in the UK receive general anaesthesia, but it is unknown whether nociceptive stimuli alter cortical brain activity in anaesthetised children. Time-locked electroencephalogram (EEG) responses to experimental tactile stimuli, experimental noxious stimuli, and clinically required cannulation were examined in 51 children (ages 1–12 years) under sevoflurane monoanaesthesia. Based on a pilot study (n = 12), we hypothesised that noxious stimulation in children receiving sevoflurane monoanaesthesia would evoke an increase in delta activity. This was tested in an independent sample of children (n = 39), where a subset (n = 11) had topical local anaesthetic applied prior to stimulation. A novel method of time-locking the stimuli to the EEG recording was developed using an event detection interface and high-speed camera. Clinical cannulation evoked a significant increase (34.2 ± 8.3%) in delta activity (P = 0.042), without concomitant changes in heart rate or reflex withdrawal, which was not observed when local anaesthetic was applied (P = 0.30). Experimental tactile (P = 0.012) and noxious (P = 0.0099) stimulation also evoked significant increases in delta activity, but the magnitude of the response was graded with stimulus intensity, with the greatest increase evoked by cannulation. We demonstrate that experimental and clinically essential noxious procedures, undertaken in anaesthetised children, alter the pattern of EEG activity, that this response can be inhibited by local anaesthetic, and that this measure is more sensitive than other physiological indicators of nociception. This technique provides the possibility that sensitivity to noxious stimuli during anaesthesia could be investigated in other clinical populations.
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paper_title: Brain regions associated with psychological pain: implications for a neural network and its relationship to physical pain

paper_content:

Research on brain areas involved in experiencing emotion and physical pain is abundant; however, psychological pain has received little attention in studies of the brain. The purpose of this systematic review was to provide an overview of studies on brain function related to psychological pain. The review was limited to studies in which participants experienced actual psychological pain or recalled a significant autobiographical event that may be assumed to have involved psychological pain. Based on results of the studies (N = 18), a tentative neural network for psychological pain is proposed that includes the thalamus, anterior and posterior cingulate cortex, the prefrontal cortex, cerebellum, and parahippocampal gyrus. Results indicated that grief may be a more accurate exemplar of psychological pain than recalled sadness, with indications of greater arousal during psychological pain. The proposed neural network for psychological pain overlaps to some extent with brain regions involved in physical pain, but results suggest a markedly reduced role for the insula, caudate, and putamen during psychological pain. Psychological pain is well known for its association with depression and as a precursor of suicidal behavior. Thus, identification of brain areas involved in psychological pain may help guide development of interventions to decrease mortality and morbidity.
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paper_title: State classification of heart rate variability by an artificial neural network in frequency domain

paper_content:

This paper examines the feasibility of accurate state classification of autonomic nervous activity (ANA) based on the power spectral pattern of the heart rate fluctuations (HRFs). Some attempts have been made to utilize artificial neural networks (ANNs) to classify HRFs for clinical diagnoses such as ischemic cardiomyopathy, arrhythmia or sleep apnea. To establish the firm bases for making such clinical diagnoses, it may be important to examine the classification accuracy for the data in physiologically well defined conditions by e.g. application of autonomic blocking agents. In this paper the three layered perceptron has been trained by the heart rate data in variety of ANS states yielded by the application of Atropine and Propranolol to 14 healthy male subjects. Six state (control, atropine and propranolol for each of the spine and upright posture) classification based on power spectrum showed average sensitivity of 67.2% and specificity 91.2%. Four state (control, atropine, propranolol and double block for either spine or upright posture) resulted in the average classification sensitivity of 75.7% and specificity 95.5%. The paper revealed that entropy bandwidth and indices originated from characteristic oscillations of blood pressure change improve the classification accuracy.
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paper_title: Heel lancing in term new-born infants: an evaluation of pain by frequency domain analysis of heart rate variability

paper_content:

The aim of the investigation was to assess pain by frequency domain analysis of heart rate variability (HRV) during a routine heel lancing procedure in term new-born infants. Beat-to-beat heart rate (HR) was recorded in 23 healthy new-born infants on the maternity ward during blood sampling for neonatal screening. A sham heel prick prior to the sharp lancing procedure was performed randomly in half of the infants. Spectral analysis of HRV was assessed for each of the following sequences: (1) baseline (2) sham heel prick (3) sharp heel prick and (4) squeezing the heel for blood sampling. The response to the sham prick did not differ significantly from the sharp prick. Compared with the baseline, sharp lancing gave rise to minor increases in HR and variability in the low frequency band of the spectral analysis. A clear stress response was provoked when the heel was squeezed for blood sampling, indicated by an increased HR and a decreased spectral power in the high frequency band (i.e. lower vagal tone). The different stress responses during the lancing and the squeezing of the heel were clearly illustrated when principal component analysis was applied and the vectors for the changes in HR and spectral pattern were indicated. In conclusion, the squeezing of the heel is the most stressful event during the heel prick procedure.
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paper_title: Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation

paper_content:

Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.
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paper_title: The sympathetic nervous system in pain.

paper_content:

Pain and similar stressful behavioural states are characterized by organized neural responses of the somatomotor, somatosensory (analgesia), autonomic and neuroendocrine systems. These defensive behaviours consist of confrontational defence, flight and quiescence. These integrated reactions of the sympathetic nervous system are well-orchestrated responses of the different sympathetic pathways to cardiovascular and other target organs which prepare the body to meet threatening challenges. The neural basis of the autonomic reactions is to be found in the target-organ specific organization of the sympathetic pathways in the periphery and neuraxis. The spinal reflex pathways are relatively specific for putative nociceptive responses of the sympathetic nervous systems. The medulla oblongata and hypothalamus contain the circuits for the specific homeostatic regulations in which the sympathetic nervous system is involved. The mesencephalon (lateral and ventrolateral periaqueductal grey) and hypothalamus contain the neuronal programmes for the defensive mechanisms which are switched on when the organism is in pain and under stress.
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paper_title: State classification of heart rate variability by an artificial neural network in frequency domain

paper_content:

This paper examines the feasibility of accurate state classification of autonomic nervous activity (ANA) based on the power spectral pattern of the heart rate fluctuations (HRFs). Some attempts have been made to utilize artificial neural networks (ANNs) to classify HRFs for clinical diagnoses such as ischemic cardiomyopathy, arrhythmia or sleep apnea. To establish the firm bases for making such clinical diagnoses, it may be important to examine the classification accuracy for the data in physiologically well defined conditions by e.g. application of autonomic blocking agents. In this paper the three layered perceptron has been trained by the heart rate data in variety of ANS states yielded by the application of Atropine and Propranolol to 14 healthy male subjects. Six state (control, atropine and propranolol for each of the spine and upright posture) classification based on power spectrum showed average sensitivity of 67.2% and specificity 91.2%. Four state (control, atropine, propranolol and double block for either spine or upright posture) resulted in the average classification sensitivity of 75.7% and specificity 95.5%. The paper revealed that entropy bandwidth and indices originated from characteristic oscillations of blood pressure change improve the classification accuracy.
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paper_title: A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features

paper_content:

An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems
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paper_title: Classification of cardiac abnormalities using heart rate signals

paper_content:

The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%.
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paper_title: Heel lancing in term new-born infants: an evaluation of pain by frequency domain analysis of heart rate variability

paper_content:

The aim of the investigation was to assess pain by frequency domain analysis of heart rate variability (HRV) during a routine heel lancing procedure in term new-born infants. Beat-to-beat heart rate (HR) was recorded in 23 healthy new-born infants on the maternity ward during blood sampling for neonatal screening. A sham heel prick prior to the sharp lancing procedure was performed randomly in half of the infants. Spectral analysis of HRV was assessed for each of the following sequences: (1) baseline (2) sham heel prick (3) sharp heel prick and (4) squeezing the heel for blood sampling. The response to the sham prick did not differ significantly from the sharp prick. Compared with the baseline, sharp lancing gave rise to minor increases in HR and variability in the low frequency band of the spectral analysis. A clear stress response was provoked when the heel was squeezed for blood sampling, indicated by an increased HR and a decreased spectral power in the high frequency band (i.e. lower vagal tone). The different stress responses during the lancing and the squeezing of the heel were clearly illustrated when principal component analysis was applied and the vectors for the changes in HR and spectral pattern were indicated. In conclusion, the squeezing of the heel is the most stressful event during the heel prick procedure.
---
paper_title: Noxious stimulation in children receiving general anaesthesia evokes an increase in delta frequency brain activity

paper_content:

More than 235,000 children/year in the UK receive general anaesthesia, but it is unknown whether nociceptive stimuli alter cortical brain activity in anaesthetised children. Time-locked electroencephalogram (EEG) responses to experimental tactile stimuli, experimental noxious stimuli, and clinically required cannulation were examined in 51 children (ages 1–12 years) under sevoflurane monoanaesthesia. Based on a pilot study (n = 12), we hypothesised that noxious stimulation in children receiving sevoflurane monoanaesthesia would evoke an increase in delta activity. This was tested in an independent sample of children (n = 39), where a subset (n = 11) had topical local anaesthetic applied prior to stimulation. A novel method of time-locking the stimuli to the EEG recording was developed using an event detection interface and high-speed camera. Clinical cannulation evoked a significant increase (34.2 ± 8.3%) in delta activity (P = 0.042), without concomitant changes in heart rate or reflex withdrawal, which was not observed when local anaesthetic was applied (P = 0.30). Experimental tactile (P = 0.012) and noxious (P = 0.0099) stimulation also evoked significant increases in delta activity, but the magnitude of the response was graded with stimulus intensity, with the greatest increase evoked by cannulation. We demonstrate that experimental and clinically essential noxious procedures, undertaken in anaesthetised children, alter the pattern of EEG activity, that this response can be inhibited by local anaesthetic, and that this measure is more sensitive than other physiological indicators of nociception. This technique provides the possibility that sensitivity to noxious stimuli during anaesthesia could be investigated in other clinical populations.
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paper_title: Brain regions associated with psychological pain: implications for a neural network and its relationship to physical pain

paper_content:

Research on brain areas involved in experiencing emotion and physical pain is abundant; however, psychological pain has received little attention in studies of the brain. The purpose of this systematic review was to provide an overview of studies on brain function related to psychological pain. The review was limited to studies in which participants experienced actual psychological pain or recalled a significant autobiographical event that may be assumed to have involved psychological pain. Based on results of the studies (N = 18), a tentative neural network for psychological pain is proposed that includes the thalamus, anterior and posterior cingulate cortex, the prefrontal cortex, cerebellum, and parahippocampal gyrus. Results indicated that grief may be a more accurate exemplar of psychological pain than recalled sadness, with indications of greater arousal during psychological pain. The proposed neural network for psychological pain overlaps to some extent with brain regions involved in physical pain, but results suggest a markedly reduced role for the insula, caudate, and putamen during psychological pain. Psychological pain is well known for its association with depression and as a precursor of suicidal behavior. Thus, identification of brain areas involved in psychological pain may help guide development of interventions to decrease mortality and morbidity.
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paper_title: Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.

paper_content:

Supervised machine learning (ML) algorithms are increasingly popular tools for fMRI decoding due to their predictive capability and their ability to capture information encoded by spatially correlated voxels. In addition, an important secondary outcome is a multivariate representation of the pattern underlying the prediction. Despite an impressive array of applications, most fMRI applications are framed as classification problems and predictions are limited to categorical class decisions. For many applications, quantitative predictions are desirable that more accurately represent variability within subject groups and that can be correlated with behavioural variables. We evaluate the predictive capability of Gaussian process (GP) models for two types of quantitative prediction (multivariate regression and probabilistic classification) using whole-brain fMRI volumes. As a proof of concept, we apply GP models to an fMRI experiment investigating subjective responses to thermal pain and show GP models predict subjective pain ratings without requiring anatomical hypotheses about functional localisation of relevant brain processes. Even in the case of pain perception, where strong hypotheses do exist, GP predictions were more accurate than any region previously demonstrated to encode pain intensity. We demonstrate two brain mapping methods suitable for GP models and we show that GP regression models outperform state of the art support vector- and relevance vector regression. For classification, GP models perform categorical prediction as accurately as a support vector machine classifier and furnish probabilistic class predictions.
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paper_title: Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation

paper_content:

Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.
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paper_title: Spontaneous pain expression recognition in video sequences

paper_content:

Automatic recognition of Pain expression has potential medical significance. In this paper we present results of the application of an automatic facial expression recognition system on sequences of spontaneous Pain expression. Twenty participants were videotaped while undergoing thermal heat stimulation at nonpainful and painful intensities. Pain was induced experimentally by use of a Peltierbased, computerized thermal stimulator with a 3 × 3 cm2 contact probe. Our aim is to automatically recognize the videos where Pain was induced. We chose a machine learning approach, previously used successfully to categorize the six basic facial expressions in posed datasets [1, 2] based on the Transferable Belief Model. For this paper, we extended this model to the recognition of sequences of spontaneous Pain expression. The originality of the proposed method is the use of the dynamic information for the recognition of spontaneous Pain expression and the combination of different sensors: facial features behavior, transient features and the context of the expression study. Experimental results show good classification rates for spontaneous Pain sequences especially when we use the contextual information. Moreover the system behaviour compares favourably to the human observer in the other case, which opens promising perspectives for the future development of the proposed system.
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paper_title: Measurement of Neonatal Responses to Painful Stimuli: A Research Review

paper_content:

The measurement of neonatal responses to painful stimuli remains a significant clinical problem. Although numerous measures have been evaluated, instruments that are valid, reliable, and clinically feasible are not yet available. The purpose of this paper is to critique the studies that have been done using biochemical, physiological, and behavioral measures to evaluate neonatal responses to painful stimuli. Specific issues regarding measurement in premature and critically ill neonates are emphasized. The intent of this review and critique of the literature is to stimulate additional research into the assessment of neonatal pain.
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paper_title: Variability in pain expression characteristics in former preterm infants

paper_content:

textabstractObjectives: To document pain expression characteristics in former preterm infants in the first year of life in whom systematic evaluation and treatment of pain was provided. Methods: Data on the Modified Behavioral Pain Scale (MBPS), Visual Analogue Scale (VAS, crying time and heart rate were collected during immunization. The effect of clinical characteristics {postconception and gestational age [PCA], surgery, ventilation, fentanyl, length of stay (LOS)} on bio-behavioral parameters was evaluated. Results: 121 procedures were recorded in 49 infants. GA was 28 (25-32) weeks. PCA was 60 (34-90) weeks. Median MBPS was 8 (2-10), median crying time 44 (0-112) s. Median pre-procedural heart rate was 144 (103-201) and increased to 184 (147-220) afterwards, resulting in a relative increase of 29 (5-99)%. Median VAS score was 7 (1-10). Significant correlations of PCA with pre-procedural heart rate (r=-0.36) and with relative increase in heart rate (r=0.33) were identified. A correlation of LOS on crying time (r=0.37) was observed. Conclusions: Correlations of PCA with pre-procedural and relative increase in heart rate reflect the maturational decrease in heart rate. LOS had an effect on pain expression, potentially reflecting the impact of cumulative 'minor' procedural pain. Copyright
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paper_title: Painful data: The UNBC-McMaster shoulder pain expression archive database

paper_content:

A major factor hindering the deployment of a fully functional automatic facial expression detection system is the lack of representative data. A solution to this is to narrow the context of the target application, so enough data is available to build robust models so high performance can be gained. Automatic pain detection from a patient's face represents one such application. To facilitate this work, researchers at McMaster University and University of Northern British Columbia captured video of participant's faces (who were suffering from shoulder pain) while they were performing a series of active and passive range-of-motion tests to their affected and unaffected limbs on two separate occasions. Each frame of this data was AU coded by certified FACS coders, and self-report and observer measures at the sequence level were taken as well. This database is called the UNBC-McMaster Shoulder Pain Expression Archive Database. To promote and facilitate research into pain and augment current datasets, we have publicly made available a portion of this database which includes: 1) 200 video sequences containing spontaneous facial expressions, 2) 48,398 FACS coded frames, 3) associated pain frame-by-frame scores and sequence-level self-report and observer measures, and 4) 66-point AAM landmarks. This paper documents this data distribution in addition to describing baseline results of our AAM/SVM system. This data will be available for distribution in March 2011.
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Title: Machine-based Multimodal Pain Assessment Tool for Infants: A Review

Section 1: Introduction
Description 1: Introduce the importance of pain assessment in infants and outline the current challenges and limitations of traditional assessment methods. Explain the need for machine-based assessment tools.

Section 2: Literature Review
Description 2: Provide an overview of machine-based approaches for pain assessment, focusing on both behavioral and physiological methods.

Section 3: Behavioral-based Pain Assessment Approaches
Description 3: Discuss various behavioral indicators of pain in infants (e.g., facial expressions, crying sounds, body movements) and review existing machine-based assessment methods.

Section 4: Facial Expressions of Pain
Description 4: Explain the importance of facial expressions in pain assessment for infants and review the different techniques to analyze these expressions, including image-based, velocity-based, model-based, and FACS-based feature representations.

Section 5: Infants' Crying Sounds
Description 5: Describe how infants' crying sounds can be used to assess pain and review the methods for processing these sounds, including frequency-domain, cepstral-domain, and time-domain feature representations.

Section 6: Body Movements Analysis
Description 6: Discuss how infants' body movements can indicate pain and review existing machine-based approaches for analyzing body movements.

Section 7: Physiological-based Pain Assessment Approaches
Description 7: Review machine-based approaches that analyze physiological indicators (e.g., vital signs, brain activity) to assess pain in infants.

Section 8: Vital Signs Analysis
Description 8: Detail the relationship between vital signs and pain, and review methods for analyzing vital signs to assess pain in infants.

Section 9: Brain Regions Analysis
Description 9: Discuss the role of brain activity in pain assessment and review techniques that use brain imaging data (e.g., fMRI, NIRS) for pain analysis.

Section 10: Current Limitations and Future Directions
Description 10: Summarize the current challenges in machine-based pain assessment for infants and suggest future research directions to address these limitations.

Section 11: Multimodal Pain Assessment Tool (MPAT)
Description 11: Propose a comprehensive multimodal pain assessment tool that integrates both behavioral and physiological indicators for a continuous and minimally biased assessment of pain in infants.

Section 12: Conclusion and Future Works
Description 12: Conclude the review by summarizing the key points and outlining the future prospects for developing effective machine-based pain assessment tools for infants.