A Survey of Body Sensor Networks
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paper_title: Body Area Networks: A Survey

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Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.
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paper_title: Wireless Non-contact EEG/ECG Electrodes for Body Sensor Networks

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A wireless EEG/ECG system using non-contact sensors is presented. The system consists of a set of simple capacitive electrodes manufactured on a standard printed circuit board that can operate through fabric or other insulation. Each electrode provides 46dB of gain over a .7-100Hz bandwidth with a noise level of 3.80uV RMS for high quality brain and cardiac recordings. Signals are digitized directly on top of the electrode and transmitted in a digital serial daisy chain, minimizing the number of wires required on the body. A small wireless base unit transmits EEG/ECG telemetry to a computer for storage and processing.
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paper_title: EcoIMU: A Dual Triaxial-Accelerometer Inertial Measurement Unit for Wearable Applications

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This paper describes EcoIMU, a gyro-free, wearable inertial measurement unit (IMU) built with a pair of triaxial accelerometers that are spatially separated and wirelessly connected on the body. It can output the translation and rotation of the body for the purpose of motion tracking and dead reckoning applications. It mitigates error accumulation and drift problems using domain knowledge including pause identification and geometric constraints on the two nodes. Experimental results show EcoIMU to have less drift error while consuming significantly lower power than comparable IMUs built with MEMS gyroscopes and accelerometers.
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paper_title: An active electrode for biopotential recording from small localized bio-sources

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BackgroundLaser bio-stimulation is a well-established procedure in Medical Acupuncture. Nevertheless there is still a confusion as to whether it works or the effect is just placebo. Although a plethora of scientific papers published, showing positive clinical results, there is still a lack of objective scientific proofs about the bio-stimulation effect of lasers used in Acupuncture. The objective of this work was to design and build a body surface electrode and an amplifier for biopotential recording from acupuncture points, considered here as small localized bio-sources (SLB). The design is aimed for studying SLB potentials provoked by laser stimulus, in search for objective proofs of the bio-stimulation effect of lasers used in Medical Acupuncture.MethodsThe active electrode presented features a new adjustable anchoring system and fractionation of the biopotential amplifier between the electrode and the cabinet's location. The new adjustable electrode anchoring system is designed to reduce the electrode-skin contact impedance, its variation and motion artifacts. That is achieved by increasing the electrode-skin tension and decreasing its relative movement. Additionally the sensing element provides local constant skin stretching thus eliminating the contribution of the skin potential artifact. The electrode is attached to the skin by a double-sided adhesive pad, where the sensing element is a stainless steel, 4 mm in diameter. The fractionation of the biopotential amplifier is done by incorporating the amplifier's front-end op-amps at the electrodes, thus avoiding the use of extra buffers. The biopotential amplifier features two selectable modes of operation: semi-AC-mode with a -3 dB bandwidth of 0.32–1000 Hz and AC-mode with a bandwidth of 0.16–1000 Hz.ResultsThe average measured DC electrode-skin contact impedance of the proposed electrode was 450 kΩ, with electrode tension of 0.3 kg/cm2 on an unprepared skin of the inner forearm. The peak-to-peak noise voltage measured at the amplifier output, with input terminals connected to common, was 10 mVp-p, or 2 μVp-p referred to the input. The common-mode rejection ratio of the amplifier was 96 dB at 50 Hz, measured with imbalanced electrodes' impedances. The prototype was also tested practically and sample records were obtained after a low intensity SLB laser stimulation. All measurements showed almost a complete absence of 50 Hz interference, although no electrolyte gel or skin preparation was applied.ConclusionThe results showed that the new active electrode presented significantly reduced the electrode-skin impedance, its variation and motion artifact influences. This allowed SLB signals with relatively high quality to be recorded without skin preparation. The design offers low noise and major reduction in parts, size and power consumption. The active electrode specifications were found to be better or at least comparable to those of other existing designs.
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paper_title: Recognition of hand movements using wearable accelerometers

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Accelerometer based activity recognition systems have typically focused on recognizing simple ambulatory activities of daily life, such as walking, sitting, standing, climbing stairs, etc. In this work, we developed and evaluated algorithms for detecting and recognizing short duration hand movements (lift to mouth, scoop, stir, pour, unscrew cap). These actions are a part of the larger and complex Instrumental Activities of Daily Life (IADL) making a drink and drinking. We collected data using small wireless tri-axial accelerometers worn simultaneously on different parts of the hand. Acceleration data for training was collected from 5 subjects, who also performed the two IADLs without being given specific instructions on how to complete them. Feature vectors (mean, variance, correlation, spectral entropy and spectral energy) were calculated and tested on three classifiers (AdaBoost, HMM, k-NN). AdaBoost showed the best performance, with an overall accuracy of 86% for detecting each of these hand actions. The results show that although some actions are recognized well with the generalized classifer trained on the subject-independent data, other actions require some amount of subject-specific training. We also observed an improvement in the performance of the system when multiple accelerometers placed on the right hand were used.
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paper_title: Power and Area Efficient Wavelet-Based On-chip ECG Processor for WBAN

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This paper proposes a power and area efficient electrocardiogram (ECG) signal processing application specific integrated circuits (ASIC) for wireless body area networks (WBAN). This signal processing ASIC can accurately detect the QRS peak with high frequency noise suppression. The proposed ECG signal processor is implemented in 0.18μm CMOS technology. It occupies only 1.2mm2 in area and 9μW in power consumption. Therefore, this ECG processor is convenient for long-term monitoring of cardio-vascular condition of patients, and is very suitable for on-body WBAN applications.
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paper_title: Sensor Placement for Activity Detection Using Wearable Accelerometers

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Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose interesting research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a framework that can answer the following questions: (i) What is the ideal sensor location for a given group of activities? (ii) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are most relevant for discriminating different activity types?
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paper_title: Clubfoot Pattern Recognition towards Personalized Insole Design

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personalized insole design is a novel approach for better quality of daily life. In this study we developed a low-cost foot pressure measurement system elaborated for primary care and community hospitals, subsequently the feature extraction and pattern recognition were carried out in order to assist the clubfoot diagnosis. The original data were obtained from 20 adults with normal feet and 30 patients with diagnosed clubfeet. Features such as peak pressure and regional contact area were deduced from 10 anatomically significant areas. It was indicated that, comparing with normal feet, flat feet exhibited larger contact area in midfoot (p
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paper_title: Respiratory Rate and Flow Waveform Estimation from Tri-axial Accelerometer Data

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There is a strong medical need for continuous, unobstrusive respiratory monitoring, and many shortcomings to existing methods. Previous work shows that MEMS accelerometers worn on the torso can measure inclination changes due to breathing, from which a respiratory rate can be obtained. There has been limited validation of these methods. The problem of practical continuous monitoring, in which patient movement disrupts the measurements and the axis of interest changes, has also not been addressed. We demonstrate a method based on tri-axial accelerometer data from a wireless sensor device, which tracks the axis of rotation and obtains angular rates of breathing motion. The resulting rates are validated against gyroscope measurements and show high correlation to flow rate measurements using a nasal cannula. We use a movement detection method to classify periods in which the patient is static and breathing signals can be observed accurately. Within these periods we obtain a close match to cannula measurements, for both the flow rate waveform and derived respiratory rates, over multi-hour datasets obtained from wireless sensor devices on hospital patients. We discuss future directions for improvement and potential methods for estimating absolute airflow rate and tidal volume.
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paper_title: Breathing Feedback System with Wearable Textile Sensors

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Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straight forward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician.
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paper_title: On distributed fault-tolerant detection in wireless sensor networks

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In this paper, we consider two important problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both the noise-related measurement error and sensor fault simultaneously in fault-tolerant detection and 2) how to choose a proper neighborhood size n for a sensor node in fault correction such that the energy could be conserved. We propose a fault-tolerant detection scheme that explicitly introduces the sensor fault probability into the optimal event detection process. We mathematically show that the optimal detection error decreases exponentially with the increase of the neighborhood size. Experiments with both Bayesian and Neyman-Pearson approaches in simulated sensor networks demonstrate that the proposed algorithm is able to achieve better detection and better balance between detection accuracy and energy usage. Our work makes it possible to perform energy-efficient fault-tolerant detection in a wireless sensor network.
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paper_title: A 20 µW contact impedance sensor for wireless body-area-network transceiver

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A low power contact impedance sensor (CIS) is presented for an energy efficient wireless body-area-network (WBAN) transceiver using a human body as a transmission medium. The proposed CIS adopts the capacitive sensing technique based on the LC resonance for detecting the parasitic capacitance between the electrodes and the human body to automatically turn on or off the transceiver. The 3'b resistive sensor, combined with a reconfigurable output driver in the transmitter, is proposed to compensate the channel quality degradation due to the contact impedance variation. It can reduce both the linearity and sensitivity requirements of the receiver front-end by 7 dB. It leads to significantly reduce the power of the LNA more than 70 % (from 2.0 mW to 0.6 mW). The proposed CIS of 1.0 mm · 1.4 mm is fabricated in 0.18 µm CMOS technology, and dissipates only 20 µW from 1.0 V.
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paper_title: A Low Power Wake-Up Circuitry Based on Dynamic Time Warping for Body Sensor Networks

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Enhancing the wear ability and reducing the form factor often are among the major objectives in design of wearable platforms. Power optimization techniques will significantly reduce the form factor and/or will prolong the time intervals between recharges. In this paper, we propose an ultra low power programmable architecture based on Dynamic Time Warping specifically designed for wearable inertial sensors. The low power architecture performs the signal processing merely as fast as the production rate for the inertial sensors, and further considers the minimum bit resolution and the number of samples that are just enough to detect the movement of interest. Our results show that the power consumption for inertial based monitoring systems can be reduced by at least three orders of magnitude using our proposed architecture compared to the state-of-the-art low power microcontrollers.
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paper_title: Enzyme catalysed biofuel cells

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Biofuel cells are energy conversion devices that use biocatalysts to convert chemical energy to electrical energy. Over the last decade, research in this area has intensified, especially in the area of direct electron transfer between enzymes and electrodes. This review details the fundamentals of enzymatic biofuel cells and reviews characterization techniques that can be used to evaluate and optimize biofuel cells. The review details the advantages and disadvantages of mediated and direct bioelectrocatalysis at electrodes for biofuel cells. It also compares and contrasts different enzyme immobilization techniques and different electrode structures.
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paper_title: A 65 nm Sub-$V_{t}$ Microcontroller With Integrated SRAM and Switched Capacitor DC-DC Converter

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Aggressive supply voltage scaling to below the device threshold voltage provides significant energy and leakage power reduction in logic and SRAM circuits. Consequently, it is a compelling strategy for energy-constrained systems with relaxed performance requirements. However, effects of process variation become more prominent at low voltages, particularly in deeply scaled technologies. This paper presents a 65 nm system-on-a-chip which demonstrates techniques to mitigate variation, enabling sub-threshold operation down to 300 mV. A 16-bit microcontroller core is designed with a custom sub-threshold cell library and timing methodology to address output voltage failures and propagation delays in logic gates. A 128 kb SRAM employs an 8 T bit-cell to ensure read stability, and peripheral assist circuitry to allow sub-Vt reading and writing. The logic and SRAM function in the range of 300 mV to 600 mV, consume 27.2 pJ/cycle at the optimal V DD of 500 mV, and 1 muW standby power at 300 mV. To supply variable voltages at these low power levels, a switched capacitor DC-DC converter is integrated on-chip and achieves above 75% efficiency while delivering between 10 muW to 250 muW of load power.
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paper_title: Physical Movement Monitoring Using Body Sensor Networks: A Phonological Approach to Construct Spatial Decision Trees

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Monitoring human activities using wearable sensor nodes has the potential to enable many useful applications for everyday situations. Limited computation, battery lifetime and communication bandwidth make efficient use of these platforms crucial. In this paper, we introduce a novel classification model that identifies physical movements from body-worn inertial sensors while taking collaborative nature and limited resources of the system into consideration. Our action recognition model uses a decision tree structure to minimize the number of nodes involved in classification of each action. The decision tree is constructed based on the quality of action recognition in individual nodes. A clustering technique is employed to group similar actions and measure quality of per-node identifications. We pose an optimization problem for finding a minimal set of sensor nodes contributing to the action recognition. We then prove that this problem is NP-hard and provide fast greedy algorithms to approximate the solution. Finally, we demonstrate the effectiveness of our distributed algorithm on data collected from five healthy subjects. In particular, our system achieves a 72.4% reduction in the number of active nodes while maintaining 93.3% classification accuracy.
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paper_title: Sensor Placement for Activity Detection Using Wearable Accelerometers

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Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose interesting research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a framework that can answer the following questions: (i) What is the ideal sensor location for a given group of activities? (ii) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are most relevant for discriminating different activity types?
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paper_title: Using Multi-modal Sensing for Human Activity Modeling in the Real World

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Traditionally smart environments have been understood to represent those (often physical) spaces where computation is embedded into the users’ surrounding infrastructure, buildings, homes, and workplaces. Users of this “smartness” move in and out of these spaces. Ambient intelligence assumes that users are automatically and seamlessly provided with context-aware, adaptive information, applications and even sensing – though this remains a significant challenge even when limited to these specialized, instrumented locales. Since not all environments are “smart” the experience is not a pervasive one; rather, users move between these intelligent islands of computationally enhanced space while we still aspire to achieve a more ideal anytime, anywhere experience. Two key technological trends are helping to bridge the gap between these smart environments and make the associated experience more persistent and pervasive. Smaller and more computationally sophisticated mobile devices allow sensing, communication, and services to be more directly and continuously experienced by user. Improved infrastructure and the availability of uninterrupted data streams, for instance location-based data, enable new services and applications to persist across environments.
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paper_title: Wireless sensor networks: a survey

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This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.
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paper_title: Multimodal analysis of body sensor network data streams for real-time healthcare

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Fundamental advances in low power circuits, wireless communication, physiological sensor design, and multimedia stream processing, have led to the deployment of body sensor networks for the real-time monitoring of individual health in diverse settings. In this paper we will present a summary of the state-of-the-art in the design and development of aggregation, processing, analysis, and retrieval techniques for body sensor network data streams. In particular, we will focus on multi-modal stream analysis techniques, in distributed and resource constrained environments, for effective real-time healthcare applications. We will describe the associated research challenges ranging from designing novel applications and mining algorithms to systems issues of resource-adaptation, reliability etc., and the intersection of these. We will also present practical deployments and emerging applications of body sensor networks both in individual healthcare as well as in applications for large-scale public health tracking of communities. We will conclude with a summary of open challenges in the field.
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paper_title: Characterization of implantable biosensor membrane biofouling.

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The material-tissue interaction that results from sensor implantation is one of the major obstacles in developing viable, long-term implantable biosensors. Strategies useful for the characterization and modification of sensor biocompatibility are widely scattered in the literature, and there are many peripheral studies from which useful information can be gleaned. The current paper reviews strategies suitable for addressing biofouling, one aspect of biosensor biocompatibility. Specifically, this paper addresses the effect of membrane biofouling on sensor sensitivity from the standpoint of glucose transport limitations. Part I discusses the in vivo and in vitro methods used to characterize biofouling and the effects of biofouling on sensor performance, while Part II presents techniques intended to improve biosensor biocompatibility.
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paper_title: Portable Preimpact Fall Detector With Inertial Sensors

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Falls and the resulting hip fractures in the elderly are a major health and economic problem. The goal of this study was to investigate the feasibility of a portable preimpact fall detector in detecting impending falls before the body impacts on the ground. It was hypothesized that a single sensor with the appropriate kinematics measurements and detection algorithms, located near the body center of gravity, would be able to distinguish an in-progress and unrecoverable fall from nonfalling activities. The apparatus was tested in an array of daily nonfall activities of young (n = 10) and elderly (n = 14) subjects, and simulated fall activities of young subjects. A threshold detection method was used with the magnitude of inertial frame vertical velocity as the main variable to separate the nonfall and fall activities. The algorithm was able to detect all fall events at least 70 ms before the impact. With the threshold adapted to each individual subject, all falls were detected successfully, and no false alarms occurred. This portable preimpact fall detection apparatus will lead to the development of a new generation inflatable hip pad for preventing fall-related hip fractures.
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paper_title: Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

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This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports. The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing, segmentation, feature extraction, dimensionality reduction and classification. For each step, we present the main techniques utilized, their advantages and drawbacks, performance metrics and usage examples. We also discuss the research challenges, such as user behavior and technical limitations, as well as the remaining open research questions.
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paper_title: ECG-Cryptography and Authentication in Body Area Networks

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Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data commnications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.
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paper_title: Addressing Context Awareness Techniques in Body Sensor Networks

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Context awareness in Body Sensor Networks (BSNs) has the significance of associating physiological user activity and environment to the sensed signals of the user. The context information derived in a BSN can be used in pervasive healthcare monitoring for relating importance to events and specifically for accurate episode detection. In this paper, we address the issues of context-aware sensing in BSNs, along with a comparison of different techniques for deducing context awareness, namely, Artificial Neural Networks, Bayesian Networks, and Hidden Markov Models.
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paper_title: Real-time physical activity monitoring by data fusion in body sensor networks

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A physical activity monitoring system by data fusion in body sensor networks is presented in this paper, which targets at providing body status information in real time and identifying body activities. By fusion of data collected from several accelerometer sensors placed on different parts of the body, the activities can be identified and tracked Mathematical approaches employed in the system include Kalman filter and hidden Markov model. With the proposed system architecture, these algorithms are distributed to different components of the system. The proposed system is applied to monitoring and identifying daily activities in laboratory and comparatively intensive activities in a gym room. Video-based approach is used as the benchmark to evaluate its performance. Comparative results indicate that, by using the proposed system, body status of daily activities can be estimated with good accuracy in real time, and body activity is identified with high accuracy within short system latency.
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paper_title: COORDINATED SENSING OF NETWORKED BODY SENSORS USING MARKOV DECISION PROCESSES

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This article describes a Markov decision process MDP framework for coordinated sensing between correlated sensors in a body-area network. The technique is designed to extend the life of mobile continuous health-monitoring systems based on energy-constrained wearable sensors. The technique enables distributed sensors in a body-area network to adapt their sampling rates in response to changing criticality of the detected data and the limited energy reserve at each sensor node. The relationship between energy consumption, sampling rates, and utility of coordinated measurements is formulated as an MDP. This MDP is solved to generate a globally optimal policy that specifies the sampling rates for each sensor for all possible states of the system. This policy is computed offline before deployment and only the resulting policy is stored within each sensor node. We also present a method of executing the global policy without requiring continuous communication between the sensors. Each sensor node maintains a local estimate of the global state. Communication occurs only when an information-theoretic model of the uncertainty in the local state estimates exceeds a predefined threshold. We show results on simulated data that demonstrate the efficacy of this distributed-control framework and compare the performance of the proposed controller with other policies.
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paper_title: Protein Engineering for Biosensors

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In Chapter 2, we introduced the basic concept of electrochemical sensors and biosensors. In this chapter, we will focus on the biological aspects of biosensors in two important regards; the first being the biological molecules involved in the molecular recognition process that gives the biosensors their specificity and sensitivity as illustrated in Figure 3.1 [1]. We will discuss how these proteins can be engineered to improve sensor performance. The second aspect is concerned with biocompatibility, which is the mutual interaction between the sensor and the tissue within which it is located. Although progress has been made in making implantable biosensors reliable and robust over a period of days, there are still significant technical issues associated with long-term (weeks to months) implantation. This reflects in part the response of the tissue to trauma and in part the inherent robustness of the biological molecules used in the sensor. This implies that the solution to long-term implantation will come from a combination of factors including minimally invasive implantation, understanding and modulating tissue response to implantation, and modifying the properties of the biomolecules. Molecular recognition occurring at or near the sensor surface can be transduced through a variety of different physical sensing modalities and this leads to a sensor classification shown below.
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paper_title: Information fusion for wireless sensor networks: Methods, models, and classifications

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Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.
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paper_title: Handbook of Multisensor Data Fusion

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Multisensor Data Fusion D. L. Hall and J. Llinas Data Fusion Perspectives and Its Role in Information Processing O. Kessler and F. White Revisions to the JDL Data Fusion Model A. N. Steinberg and C. L. Bowman Introduction to the Algorithms of Data Association in Multiple-Target Tracking J. K. Uhlmann The Principles and Practice of Image and Spatial Data Fusion E. Waltz and T. Waltz Data Registration R. R. Brooks and L. Grewe Data Fusion Automation: A Top-Down Perspective R. Antony Overview of Distributed Decision Fusion M. E. Liggins Introduction to Particle Filtering: The Next Stage in Tracking M. E. Liggins and K-C. Chang Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors T. Kirubarajan and Y. Bar-Shalom An Introduction to the Combinatorics of Optimal and Approximate Data Association J. K. Uhlmann A Bayesian Approach to Multiple-Target Tracking L. D. Stone Data Association Using Multiple-Frame Assignments A. B. Poore, S. Lu, and B. J. Suchomel General Decentralized Data Fusion with Covariance Intersection S. Julier and J. K. Uhlmann Data Fusion in Nonlinear Systems S. Julier and J. K. Uhlmann Random Set Theory for Multisource-Multitarget Information Fusion R. Mahler Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture M. E. Liggins and K-C. Chang Foundations of Situation and Threat Assessment A. N. Steinberg An Introduction to Level 5 Fusion: The Role of the User E. Blasch Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness using Advanced Human-Computer Interfaces D. L. Hall, C. M. Hall, and S. A. H. McMullen Requirements Derivation for Data Fusion Systems E. Waltz and D. L. Hall A Systems Engineering Approach for Implementing Data Fusion Systems C. L. Bowman and A. N. Steinberg Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques J. Llinas, Capt. L. McConnell, C. L. Bowman, D. L. Hall, and P. Applegate Data Management Support to Tactical Data Fusion R. Antony Assessing the Performance of Multisensor Fusion Processes J. Llinas A Survey of COTS Software for Multisensor Data Fusion S. A. Hall McMullen, R. R. Sherry, and S. Miglani A Survey of Multisensor Data Fusion Systems M. L. Nichols Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems C. S. Byington and A. K. Garga Adapting Data Fusion to Chemical and Biological Sensors D. C. Swanson Fusion of Ground and Satellite Data via the Army Battle Command System S. Aungst, M. Campbell, J. Kuhns, D. Beyerle, T. Bacastow, and J. Knox Developing Information Fusion Methods for Combat Identification T. M. Schuck, J. Bockett Hunter, and D. D. Wilson
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paper_title: Combination of body sensor networks and on-body signal processing algorithms: the practical case of MyHeart project

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Smart clothes increase the efficiency of long-term non-invasive monitoring systems by facilitating the placement of sensors and increasing the number of measurement locations. Since the sensors are either garment-integrated or embedded in an unobtrusive way in the garment, the impact on the subject's comfort is minimized. However, the main challenge of smart clothing lies in the enhancement of signal quality and the management of the huge data volume resulting from the variable contact with the skin, movement artifacts, non-accurate location of sensors and the large number of acquired signals. This paper exposes the strategies and solutions adopted in the European 1ST project MyHeart to address these problems, from the definition of the body sensor network to the description of two embedded signal processing techniques performing on-body ECG enhancement and motion activity classification.
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paper_title: A context-aware service platform to support continuous care networks for home-based assistance

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Efficient and effective treatment of chronic disease conditions requires the implementation of emerging continuous care models. These models pose several technology-oriented challenges for home-based continuous care, requiring assistance services based on collaboration among different stakeholders: health operators, patient relatives, as well as social community members. This work describes a context-aware service platform designed for improving patient quality of life by supporting care team activity, intervention and cooperation. Leveraging on an ontology-based context management middleware, the proposed architecture exploits information coming from biomedical and environmental sensing devices and from patient social context in order to automate context-aware patient case management, especially for alarm detection and management purposes.
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paper_title: Activity identification using body-mounted sensors—a review of classification techniques

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With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
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paper_title: Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

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This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy of our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activities preliminarily and recognizes these two different types of activities separately. Since multilayer neural networks can generate complex discriminating surfaces for recognition problems, we adopt neural networks as the classifiers for activity recognition. An effective feature subset selection approach has been developed to determine significant feature subsets and compact classifier structures with satisfactory accuracy. Experimental results have successfully validated the effectiveness of the proposed recognition scheme.
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paper_title: Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

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This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports. The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing, segmentation, feature extraction, dimensionality reduction and classification. For each step, we present the main techniques utilized, their advantages and drawbacks, performance metrics and usage examples. We also discuss the research challenges, such as user behavior and technical limitations, as well as the remaining open research questions.
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paper_title: Data Compression by Temporal and Spatial Correlations in a Body-Area Sensor Network: A Case Study in Pilates Motion Recognition

paper_content:

We consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.
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paper_title: A combined method to reduce motion artifact and power line interference for wearable healthcare systems

paper_content:

A combined method to reduce motion artifact (MA) and power line interference (PLI) for wearable healthcare system is introduced. The proposed method has a block for the reduction of MA using measured electrode-skin impedance as a reference signal and a block for the cancellation of PLI using mixed-signal feedback to relax the dynamic range requirements of components in the forward path. The measured electrode-skin impedance is used as a reference signal of an adaptive filter for reducing MA. The 60Hz notch frequency which has the same amplitude as PLI's is made digitally and uses feedback to the input stage to reduce PLI. This method was simulated using MATLAB/Simulink for high-level verification of the method's validity.
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paper_title: QRS Detection Based on Morphological Filter and Energy Envelope for Applications in Body Sensor Networks

paper_content:

Emerging body sensor networks (BSN) provide solutions for continuous health monitoring at anytime and from anywhere. The implementation of these monitoring solutions requires wearable sensor devices and thus creates new technology challenges in both software and hardware. This paper presents a QRS detection method for wearable Electrocardiogram (ECG) sensor in body sensor networks. The success of proposed method is based on the combination of two computationally efficient procedures, i.e., single-scale mathematical morphological (MM) filter and approximated envelope. The MM filter removes baseline wandering, impulsive noise and the offset of DC component while the approximated envelope enhances the QRS complexes. The performance of the algorithm is verified with standard MIT-BIH arrhythmia database as well as exercise ECG data. It achieves a low detection error rate of 0.42% based on the MIT-BIH database.
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paper_title: Fuzzy Soft Mathematical Morphology

paper_content:

A new framework which extends the concepts of soft mathematical morphology into fuzzy sets is presented. Images can be considered as arrays of fuzzy singletons on the Cartesian grid. Based on this notion the definitions for the basic fuzzy soft morphological operations are derived. Compatibility with binary soft mathematical morphology as well as the algebraic properties of fuzzy soft operations are studied. Explanation of the defined operations is also provided through several examples and experimental results.
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paper_title: An Effective QRS Detection Algorithm for Wearable ECG in Body Area Network

paper_content:

A novel QRS detection algorithm for wearable ECG devices and its FPGA implementation are presented in this paper. The proposed algorithm utilizes the hybrid opening- closing mathematical morphology filtering to suppress the impulsive noise and remove the baseline drift and uses modulus accumulation to enhance the signal. The proposed algorithm achieves an average QRS detection rate of 99.53%, a sensitivity of 99.82% and a positive prediction of 99.71% against the MIT/BIH Arrhythmia Database. It compares favorably to published methods.
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paper_title: Remote Activity Monitoring of the Elderly Using a Two-Axis Accelerometer

paper_content:

This paper presents a design and experimental study of a remote posture monitoring system. The monitoring system aims for applications in activity analysis of the elderly. We propose a wearable sensor system, which consists of a two-axis accelerometer and RF wireless communication modules. The acquired body motion signals from accelerometers are transmitted to a host computer via RF link for feature extraction and pattern recognition. Wavelet transform techniques are adopted for feature extraction of human body postures. The signal is decomposed into five levels and low-frequency components are extracted to obtain useful features. Pattern recognition techniques are then applied to distinguish five basic postures: up stairs, down stairs, walking, standing, and sitting. Experimental results are presented to show the effectiveness of the proposed method.
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paper_title: A QRS Complex Detection Algorithm Based on Mathematical Morphology and Envelope

paper_content:

The QRS complex detection is very important to ECG analysis. This paper aims to present an algorithm of QRS complex detection based on mathematical morphology and envelope. Baseline wandering is removed from ECG signal by morphological method. Then the signal gets the envelope through a low-pass filter, improving signal-to-noise .The performance of the algorithm is evaluated with MIT-BIH database. The algorithm gets the high detection rate (99.79%) and high speed
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paper_title: Power and Area Efficient Wavelet-Based On-chip ECG Processor for WBAN

paper_content:

This paper proposes a power and area efficient electrocardiogram (ECG) signal processing application specific integrated circuits (ASIC) for wireless body area networks (WBAN). This signal processing ASIC can accurately detect the QRS peak with high frequency noise suppression. The proposed ECG signal processor is implemented in 0.18μm CMOS technology. It occupies only 1.2mm2 in area and 9μW in power consumption. Therefore, this ECG processor is convenient for long-term monitoring of cardio-vascular condition of patients, and is very suitable for on-body WBAN applications.
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paper_title: Detecting and tracking dim moving point target in IR image sequence

paper_content:

This paper presents a novel algorithm for detecting and tracking dim moving point target in IR image sequence with low SNR. Original images are preprocessed using temperature non-linear elimination and Top-hat operator, and then a composite frame is obtained by reducing the three-dimensional (3D) spatio-temporal scanning for target to 2D spatial hunting. Finally the target trajectory is tracked under the condition of constant false-alarm probability (CFAR). Based on the experimental results, the algorithm can successfully detect dim moving point target and accurately estimate its trajectory. The algorithm, insensitive to the velocity mismatch and the changes of statistical distribution of background or noise, is adaptable to real-time target detection and tracking.
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paper_title: Extraction of Gait Features Using a Wireless Body Sensor Network (BSN)

paper_content:

A body sensor network (BSN) is presented in this paper, which targets at integrating physical activity signal and physiologically related features to provide detailed body context information. Issues concerning the structure of the proposed BSN, functions and interactions between its components are stressed. The BSN has been applied to conduct gait analysis in this study. A gait tracker based on Kalman filter is proposed to estimate hip angle and swing velocity of the lower limb segment using the sensor measurements. Experimental result shows that the movement of the legs during gait cycles is well revealed with the proposed method
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paper_title: Activity identification using body-mounted sensors—a review of classification techniques

paper_content:

With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
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paper_title: An Effective QRS Detection Algorithm for Wearable ECG in Body Area Network

paper_content:

A novel QRS detection algorithm for wearable ECG devices and its FPGA implementation are presented in this paper. The proposed algorithm utilizes the hybrid opening- closing mathematical morphology filtering to suppress the impulsive noise and remove the baseline drift and uses modulus accumulation to enhance the signal. The proposed algorithm achieves an average QRS detection rate of 99.53%, a sensitivity of 99.82% and a positive prediction of 99.71% against the MIT/BIH Arrhythmia Database. It compares favorably to published methods.
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paper_title: Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

paper_content:

This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports. The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing, segmentation, feature extraction, dimensionality reduction and classification. For each step, we present the main techniques utilized, their advantages and drawbacks, performance metrics and usage examples. We also discuss the research challenges, such as user behavior and technical limitations, as well as the remaining open research questions.
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paper_title: Sensor Based Time Series Classification of Body Movement

paper_content:

Advances in sensing and monitoring technology are being incorporated into today’s healthcare practice. As a result, the concept of Body Sensor Networks (BSN) has been proposed to describe the wearable/wireless devices for healthcare applications. One of the major application scenarios for BSN is to detect and classify the body movements for long-term lifestyle and healthcare monitoring. This paper introduces a new approach for analyzing the time series obtained from BSN. In our research, the BSN record the acceleration data of the volunteer’s movement while performing a set of activities such as jogging, walking, resting, and transitional activities. The main contribution of this paper is the proposed time series approximation and feature extraction algorithm that can convert the sensor-based time series data into a density map. We have performed extensive experiments to compare the accuracy in classifying the time series into different activities. It is concluded that the proposed approach would aid greatly the development of efficient health monitoring systems in the future. To the best of our knowledge, no similar research has been reported in the BSN field and we expect our research could provide useful insights for further investigation.
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paper_title: Integration of Sensing and Feedback Components for Human Motion Replication

paper_content:

Replication of human body motion is a very important means to maintain a subject’s emotion, knowledge and experience. The replication process requires accurate motion capturing system with sensor technologies to measure postures of human bodies and posture transmission, as well as feedback systems to adjust postures to fit into targeted ones. The sensing and feedback technologies are fundamental building blocks of motion capturing systems, work training system and rehabilitation systems. In particular, the construction of sensing and feedback systems for dynamic postures is much more complicated than that for static postures in terms of the time evolution and non-ridge body. We believe that dynamic postures can be represented by a set of blueprint or code like trajectories of particle of human body movement. Furthermore, we derive that the sensing and feedback systems should be able to establish to directly measure those critical particles without relying on external infrastructures. In the paper, some of those kinds of sensing and feedback devices are presented and some evidences such as feature contours are obtained through analysis of captured data by those devices in order to prove our estimation. We confess that our work is preliminary for this new field, but we hope that the work presented here can lead more efforts to bring out systematic approaches of feature detection and extraction of human postures whose characteristics are different from those of video and audio.
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paper_title: Information fusion for wireless sensor networks: Methods, models, and classifications

paper_content:

Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.
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paper_title: Stair climbing detection during daily physical activity using a miniature gyroscope

paper_content:

A new method of monitoring physical activity that is able to detect walking upstairs using a miniature gyroscope attached to the shank is presented. Wavelet transformation, in conjunction with a simple kinematics model, was used to detect toe-off, heel-strike and foot-flat, as well cycles corresponding to stair ascent. To evaluate the system, three studies were performed. The method was first tested on 10 healthy young volunteer subjects in a gait laboratory where an ultrasonic motion system was used as a reference system. In the second study, the system was tested on three hospitalized elderly people to classify walking upstairs from walking downstairs and flat walking. In the third study, monitoring was performed on seven patients with peripheral vascular disease for 60 min during their daily physical activity. The first study revealed a close relationship between the ambulatory and the reference systems. Compared to the reference system, the ambulatory system had an overall sensitivity and specificity of 98% and 97%, respectively. In the second study, the ambulatory system also showed a very high sensitivity (>94%) in identifying a 50 stairs ascent from walking on the flat and walking downstairs. Finally, compared with visual surveillance, we observed a relatively high accuracy in identifying 196 walking upstairs cycles through daily physical activity in the third study. Our results demonstrated a reliable technique of measuring walking upstairs during physical activity.
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paper_title: Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion

paper_content:

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).
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paper_title: Activity identification using body-mounted sensors—a review of classification techniques

paper_content:

With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
---
paper_title: A spatio-temporal architecture for context aware sensing

paper_content:

Context-aware sensing is an integral part of the body sensor network (BSN) design and it allows the understanding of intrinsic characteristics of the sensed signal and determination of how BSNs should react to different events and adapt its monitoring behaviour. The purpose of this paper is to propose a novel spatio-temporal self-organising map that minimises the number of neurons involved whilst maintaining a high accuracy in class separation for both static and dynamic activities.
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paper_title: Graph-Coupled HMMs for Modeling the Spread of Infection

paper_content:

We develop Graph-Coupled Hidden Markov Models (GCHMMs) for modeling the spread of infectious disease locally within a social network. Unlike most previous research in epidemiology, which typically models the spread of infection at the level of entire populations, we successfully leverage mobile phone data collected from 84 people over an extended period of time to model the spread of infection on an individual level. Our model, the GCHMM, is an extension of widely-used Coupled Hidden Markov Models (CHMMs), which allow dependencies between state transitions across multiple Hidden Markov Models (HMMs), to situations in which those dependencies are captured through the structure of a graph, or to social networks that may change over time. The benefit of making infection predictions on an individual level is enormous, as it allows people to receive more personalized and relevant health advice.
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paper_title: Recognition of hand movements using wearable accelerometers

paper_content:

Accelerometer based activity recognition systems have typically focused on recognizing simple ambulatory activities of daily life, such as walking, sitting, standing, climbing stairs, etc. In this work, we developed and evaluated algorithms for detecting and recognizing short duration hand movements (lift to mouth, scoop, stir, pour, unscrew cap). These actions are a part of the larger and complex Instrumental Activities of Daily Life (IADL) making a drink and drinking. We collected data using small wireless tri-axial accelerometers worn simultaneously on different parts of the hand. Acceleration data for training was collected from 5 subjects, who also performed the two IADLs without being given specific instructions on how to complete them. Feature vectors (mean, variance, correlation, spectral entropy and spectral energy) were calculated and tested on three classifiers (AdaBoost, HMM, k-NN). AdaBoost showed the best performance, with an overall accuracy of 86% for detecting each of these hand actions. The results show that although some actions are recognized well with the generalized classifer trained on the subject-independent data, other actions require some amount of subject-specific training. We also observed an improvement in the performance of the system when multiple accelerometers placed on the right hand were used.
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paper_title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

paper_content:

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.
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paper_title: Design and analysis of low-power body area networks based on biomedical signals

paper_content:

A body area network (BAN) as one branch of Sensor Networks, is an inter-disciplinary area which holds great promises for revolutionising the current health care systems. BAN combines the real-time updating of biomedical data with the continuous and dynamic health care monitoring closely. A number of intelligence biomedical sensors can be integrated into a wireless BAN system, and the system can be used for prevention, diagnosis and timely treatment of various medical conditions. In this article, we propose a data fusion technique for a BAN based on biomedical signals. This proposed solution is of much lower complexity than conventional techniques and hence can significantly reduce the power consumption in the BAN. The technology is carried out by removing redundant and unnecessary sample information and shifting a large portion of processing and control loads to the remote control centre in an asymmetric manner. This approach not only reduces the power consumption of biosensor nodes in a BAN, but also ens...
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paper_title: Compressive Sensing of Neural Action Potentials Using a Learned Union of Supports

paper_content:

Wireless neural recording systems are subject to stringent power consumption constraints to support long-term recordings and to allow for implantation inside the brain. In this paper, we propose using a combination of on-chip detection of action potentials ("spikes") and compressive sensing (CS) techniques to reduce the power consumption of the neural recording system by reducing the power required for wireless transmission. We empirically verify that spikes are compressible in the wavelet domain and show that spikes from different neurons acquired from the same electrode have subtly different sparsity patterns or supports. We exploit the latter fact to further enhance the sparsity by incorporating a union of these supports learned over time into the spike recovery procedure. We show, using extra cellular recordings from human subjects, that this mechanism improves the SNDR of the recovered spikes over conventional basis pursuit recovery by up to 9.5 dB (6 dB mean) for the same number of CS measurements. Though the compression ratio in our system is contingent on the spike rate at the electrode, for the datasets considered here, the mean ratio achieved for 20-dB SNDR recovery is improved from 26:1 to 43:1 using the learned union of supports.
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paper_title: Wireless sensor networks: a survey

paper_content:

This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.
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paper_title: Compressed data aggregation for energy efficient wireless sensor networks

paper_content:

As a burgeoning technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless communications. However, little work is done to apply CS to multihop networking scenarios. In this paper, we investigate the application of CS to data collection in wireless sensor networks, and we aim at minimizing the network energy consumption through joint routing and compressed aggregation. We first characterize the optimal solution to this optimization problem, then we prove its NP-completeness. We further propose a mixed-integer programming formulation along with a greedy heuristic, from which both the optimal (for small scale problems) and the near-optimal (for large scale problems) aggregation trees are obtained. Our results validate the efficacy of the greedy heuristics, as well as the great improvement in energy efficiency through our joint routing and aggregation scheme.
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paper_title: Data Compression by Temporal and Spatial Correlations in a Body-Area Sensor Network: A Case Study in Pilates Motion Recognition

paper_content:

We consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.
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paper_title: Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission

paper_content:

The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noise
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paper_title: Probabilistic routing in on-body sensor networks with postural disconnections

paper_content:

This paper presents a novel store-and-forward packet routing algorithm for Wireless Body Area Networks (WBAN) with frequent postural partitioning. A prototype WBAN has been constructed for experimentally characterizing on-body topology disconnections in the presence of ultra short range radio links, unpredictable RF attenuation, and human postural mobility. A probabilistic packet routing protocol is then developed using a stochastic link cost, reflecting the body postural trends. The performance of the proposed protocol is evaluated experimentally and via simulation, and is compared with a generic probabilistic routing protocol and a specialized on-body packet flooding mechanism that provides the routing delay lower-bounds. It is shown that via successful modeling of the spatio-temporal locality of link disconnection patterns, the proposed algorithm can provide better routing delay performance compared to the existing probabilistic routing protocols in the literature.
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paper_title: Joint active queue management and congestion control protocol for healthcare applications in wireless body sensor networks

paper_content:

Wireless Body Sensor Network (WBSN) consists of a large number of distributed sensor nodes. Wireless sensor networks are offering the next evolution in biometrics and healthcare monitoring applications. The present paper proposes a congestion control protocol based on the learning automata which prevents the congestion by controlling the source rate. Furthermore, a new active queue management mechanism is developed. The main objective of the proposed active queue management mechanism is to control and manage the entry of each packet to sensor nodes based on learning automata. The proposed system is able to discriminate different physiological signals and assign them different priorities. Thus, it would be possible to provide better quality of service for transmitting highly important vital signs. The simulation results confirm that the proposed protocol improves system throughput and reduces delay and packet dropping.
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paper_title: Using relay network to increase life time in wireless body area sensor networks

paper_content:

Wireless body area sensor networks will revolutionize health care services by remote, continuous and non-invasive monitoring. Body area sensor networks (BASN) should monitor various physiological parameters of a person for a long period of time. Thus, efficient energy usage in sensor nodes is essential in order to provide a long life time for the network. This paper investigates the effect of adding a relay network to the network of body sensors to reduce energy consumption of sensor nodes when transmitting data to the sink.
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paper_title: Probabilistic routing in on-body sensor networks with postural disconnections

paper_content:

This paper presents a novel store-and-forward packet routing algorithm for Wireless Body Area Networks (WBAN) with frequent postural partitioning. A prototype WBAN has been constructed for experimentally characterizing on-body topology disconnections in the presence of ultra short range radio links, unpredictable RF attenuation, and human postural mobility. A probabilistic packet routing protocol is then developed using a stochastic link cost, reflecting the body postural trends. The performance of the proposed protocol is evaluated experimentally and via simulation, and is compared with a generic probabilistic routing protocol and a specialized on-body packet flooding mechanism that provides the routing delay lower-bounds. It is shown that via successful modeling of the spatio-temporal locality of link disconnection patterns, the proposed algorithm can provide better routing delay performance compared to the existing probabilistic routing protocols in the literature.
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paper_title: A 5.2 mW Self-Configured Wearable Body Sensor Network Controller and a 12 $\mu$ W Wirelessly Powered Sensor for a Continuous Health Monitoring System

paper_content:

A self-configured body sensor network controller and a high efficiency wirelessly powered sensor are presented for a wearable, continuous health monitoring system. The sensor chip harvests its power from the surrounding health monitoring band using an Adaptive Threshold Rectifier (ATR) with 54.9% efficiency, and it consumes 12 μW to implement an electrocardiogram (ECG) analog front-end and an ADC. The ATR is implemented with a standard CMOS process for low cost. The adhesive bandage type sensor patch is composed of the sensor chip, a Planar-Fashionable Circuit Board (P-FCB) inductor, and a pair of dry P-FCB electrodes. The dry P-FCB electrodes enable long term monitoring without skin irritation. The network controller automatically locates the sensor position, configures the sensor type (self-configuration), wirelessly provides power to the configured sensors, and transacts data with only the selected sensors while dissipating 5.2 mW at a single 1.8 V supply. Both the sensor and the health monitoring band are implemented using P-FCB for enhanced wearability and for lower production cost. The sensor chip and the network controller chip occupy 4.8 mm 2 and 15.0 mm 2 , respectively, including pads, in standard 0.18 μm 1P6M CMOS technology.
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paper_title: Ultra-low Power Sensors with Near Field Communication for Mobile Applications

paper_content:

In this paper, we study the applicability of an emerging RFID-based communication technology, NFC (Near Field Communication), to ultra-low power wireless sensors. We present potential application examples of passive and semi-passive NFC-enabled sensors. We compare their NFC-based implementations to corresponding implementations based on short-range radios such as Bluetooth and Wibree. The comparison addresses both technical properties and usability. We also introduce Smart NFC Interface, which is our general-purpose prototype platform for rapid prototyping of NFC and Bluetooth implementations. Two pilot sensor implementations and an NFC-Bluetooth gateway implementation based on our platform are presented and evaluated. Finally, needs and possibilities for technical improvements of available NFC technology are discussed.
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paper_title: Temperature-aware routing for telemedicine applications in embedded biomedical sensor networks

paper_content:

Biomedical sensors, called invivo sensors, are implanted in human bodies, and cause some harmful effects on surrounding body tissues. Particularly, temperature rise of the invivo sensors is dangerous for surrounding tissues, and a high temperature may damage them from a long term monitoring. In this paper, we propose a thermal-aware routing algorithm, called least total-route-temperature (LTRT) protocol, in which nodes temperatures are converted into graph weights, and minimum temperature routes are obtained. Furthermore, we provide an extensive simulation evaluation for comparing several other related schemes. Simulation results show the advantages of the proposed scheme.
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paper_title: A Distributed Trust Evaluation Model and Its Application Scenarios for Medical Sensor Networks

paper_content:

The development of medical sensor networks (MSNs) is imperative for e-healthcare, but security remains a formidable challenge yet to be resolved. Traditional cryptographic mechanisms do not suffice given the unique characteristics of MSNs, and the fact that MSNs are susceptible to a variety of node misbehaviors. In such situations, the security and performance of MSNs depend on the cooperative and trust nature of the distributed nodes, and it is important for each node to evaluate the trustworthiness of other nodes. In this paper, we identify the unique features of MSNs and introduce relevant node behaviors, such as transmission rate and leaving time, into trust evaluation to detect malicious nodes. We then propose an application-independent and distributed trust evaluation model for MSNs. The trust management is carried out through the use of simple cryptographic techniques. Simulation results demonstrate that the proposed model can be used to effectively identify malicious behaviors and thereby exclude malicious nodes. This paper also reports the experimental results of the Collection Tree Protocol with the addition of our proposed model in a network of TelosB motes, which show that the network performance can be significantly improved in practice. Further, some suggestions are given on how to employ such a trust evaluation model in some application scenarios.
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paper_title: A Wireless-Interface SoC Powered by Energy Harvesting for Short-range Data Communication

paper_content:

This paper describes the design and estimation of a wireless-interface SoC for wireless battery-less mouse with short-range data communication capability. It comprises an RF transmitter and microcontroller. An SoC, which is powered by an electric generator that exploits gyration energy by dragging the mouse, was fabricated using the TSMC 0.18-mum CMOS process. Features of the SoC are the adoption of a simple FSK modulation scheme, a single-end configuration on the RF transmitter, and the specific microcontroller design for mouse operation. We verified that the RF transmitter can perform data communication with a 1-m range at 2.17 mW, and the microcontroller consumes 0.03 mW at 1 MHz, which shows that the total power consumption in the SoC is 2.2 mW. This is sufficiently low for the SoC to operate with energy harvesting
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paper_title: Plug 'n Play Simplicity for Wireless Medical Body Sensors

paper_content:

Wireless medical body sensors are a key technology for unobtrusive health monitoring. The easy setup of such wireless body area networks is crucial to protect the user from the complexity of these systems. But automatically forming a wireless network comprising all sensors attached to the same body is challenging. We present a method for making wireless body-worn medical sensors aware of the persons they belong to by combining body-coupled with wireless communication. This enables a user to create a wireless body sensor network by just sticking the sensors to her body. A personal identifier allows sensors to annotate their readings with a user ID thereby ensuring safety in personal healthcare environments with multiple users.
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paper_title: ReTrust: Attack-Resistant and Lightweight Trust Management for Medical Sensor Networks

paper_content:

Wireless medical sensor networks (MSNs) enable ubiquitous health monitoring of users during their everyday lives, at health sites, without restricting their freedom. Establishing trust among distributed network entities has been recognized as a powerful tool to improve the security and performance of distributed networks such as mobile ad hoc networks and sensor networks. However, most existing trust systems are not well suited for MSNs due to the unique operational and security requirements of MSNs. Moreover, similar to most security schemes, trust management methods themselves can be vulnerable to attacks. Unfortunately, this issue is often ignored in existing trust systems. In this paper, we identify the security and performance challenges facing a sensor network for wireless medical monitoring and suggest it should follow a two-tier architecture. Based on such an architecture, we develop an attack-resistant and lightweight trust management scheme named ReTrust. This paper also reports the experimental results of the Collection Tree Protocol using our proposed system in a network of TelosB motes, which show that ReTrust not only can efficiently detect malicious/faulty behaviors, but can also significantly improve the network performance in practice.
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paper_title: Accelerometer-based fall detection using optimized ZigBee data streaming

paper_content:

Wireless body-area sensor networks (WBSNs) are key components of e-health solutions. Wearable wireless sensors can monitor and collect many different physiological parameters accurately, economically and efficiently. In this work we focus on WBSN for fall detection applications, where the real-time nature of I/O data streams is of critical importance. Additionally, this generation of alarms promises to maximize system life. Throughput and energy efficiency of the communication protocol must also be carefully optimized. In this article we investigate ZigBee's ability to meet WBSN requirements, with higher communication efficiency and lower power consumption than a Bluetooth serial port profile (SPP) based solution. As a case study we implemented an accelerometer-based fall detection algorithm, able to detect eight different fall typologies by means of a single sensor worn on the subjects' waist. This algorithm has a low computational complexity and can be processed on an embedded platform. Fall simulations were performed by three voluntary subjects. Preliminary results are promising and show excellent values for both sensitivity and specificity. This case study showed how a ZigBee-based network can be used for high throughput WBSN scenarios.
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paper_title: ECG-Cryptography and Authentication in Body Area Networks

paper_content:

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data commnications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.
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paper_title: Investigating network architectures for body sensor networks

paper_content:

The choice of network architecture for body sensor networks is an important one because it significantly affects overall system design and performance. Current approaches use propagation models or specific medium access control protocols to study architectural choices. The issue with the first approach is that the models do not capture the effects of interference and fading. Further, the question of architecture can be raised without imposing a specific MAC protocol. In this paper, we first evaluate the star and multihop network topologies against design goals, such as power and delay efficiency. We then design experiments to investigate the behavior of electromagnetic propagation at 2.4 GHz through and around the human body. Along the way, we develop a novel visualization tool to aid in summarizing information across all pairs of nodes, thus providing a way to discern patterns in large data sets visually. Our results suggest that while a star architecture with nodes operating at low power levels might suffice in a cluttered indoor environment, nodes in an outdoor setting will have to operate at higher power levels or change to a multihop architecture to support acceptable packet delivery ratios. Through simple analysis, the potential increase in packet delivery ratio by switching to a multihop architecture is evaluated.
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paper_title: Heartbeat driven medium access control for body sensor networks

paper_content:

H-MAC is a novel Time Division Multiple Access (TDMA) based MAC protocol designed for Body Sensor Networks (BSNs). It improves energy efficiency by exploiting human heartbeat rhythm information to perform time synchronization for TDMA. Heartbeat rhythm is inherent in every human body and can be detected in a variety of biosignals. Therefore, biosensors in BSNs can extract the heartbeat rhythm from their sensory data. Moreover, all the rhythms represented by peak sequences are naturally synchronized since they are driven by the same source, the heartbeat. By following the rhythm, wireless biosensors can achieve time synchronization without having to turn on their radio to receive periodic timing information from a central controller, so that energy cost for time synchronization can be completely avoided and the lifetime of network can be prolonged. An active synchronization recovery scheme is also developed, in which two resynchronization procedures are implemented. The algorithms are verified using real world data from MIT-BIH multi-parameter database MIMIC.
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paper_title: PNP-MAC: Preemptive Slot Allocation and Non-Preemptive Transmission for Providing QoS in Body Area Networks

paper_content:

One of the most important and yet most challenging issues in Body Area Networks (BANs) is to provide diverse Quality of Service (QoS). Most physiological data monitoring applications require low rate periodic reporting while real-time entertainment applications require high rate continuous streaming. Emergency alarm, the most time-critical but unpredictable data, must be delivered instantaneously. A BAN should satisfy these diverse requirements since applications each with distinctive QoS requirement may run simultaneously. We propose PNP-MAC protocol that can flexibly handle variety of applications with diverse requirements through fast, preemptive slot allocation, non-preemptive transmission in the allocated slots, and flexible superframe adjustments. Performance evaluation using OPNET network simulator shows that PNP-MAC can satisfy diverse delay and throughput requirements of various applications such as continuous streaming, routine periodic monitoring, and time-critical emergency alarm.
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paper_title: An ultra-low-power medium access control protocol for body sensor network

paper_content:

In this paper, BSN-MAC, a medium access control (MAC) protocol designed for Body Sensor Networks (BSNs) is proposed. Due to the traffic coupling and sensor diversity characteristics of BSNs, common MAC protocols can not satisfy the unique requirements of the biomedical sensors in BSNs. BSN-MAC exploits the feedback information from the deployed sensors to form a closed-loop control of the MAC parameters. A control algorithm is proposed to enable the BSN coordinator to adjust parameters of the IEEE 802.15.4 superframe to achieve both energy efficiency and low latency on energy critical nodes. We evaluate the performance of BSN-MAC by comparing it with the IEEE 802.15.4 MAC protocol using energy efficiency as the primary metric.
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paper_title: Energy-Efficiency Analysis of a Distributed Queuing Medium Access Control Protocol for Biomedical Wireless Sensor Networks in Saturation Conditions

paper_content:

The aging population and the high quality of life expectations in our society lead to the need of more efficient and affordable healthcare solutions. For this reason, this paper aims for the optimization of Medium Access Control (MAC) protocols for biomedical wireless sensor networks or wireless Body Sensor Networks (BSNs). The hereby presented schemes always have in mind the efficient management of channel resources and the overall minimization of sensors’ energy consumption in order to prolong sensors’ battery life. The fact that the IEEE 802.15.4 MAC does not fully satisfy BSN requirements highlights the need for the design of new scalable MAC solutions, which guarantee low-power consumption to the maximum number of body sensors in high density areas (i.e., in saturation conditions). In order to emphasize IEEE 802.15.4 MAC limitations, this article presents a detailed overview of this de facto standard for Wireless Sensor Networks (WSNs), which serves as a link for the introduction and initial description of our here proposed Distributed Queuing (DQ) MAC protocol for BSN scenarios. Within this framework, an extensive DQ MAC energy-consumption analysis in saturation conditions is presented to be able to evaluate its performance in relation to IEEE 802.5.4 MAC in highly dense BSNs. The obtained results show that the proposed scheme outperforms IEEE 802.15.4 MAC in average energy consumption per information bit, thus providing a better overall performance that scales appropriately to BSNs under high traffic conditions. These benefits are obtained by eliminating back-off periods and collisions in data packet transmissions, while minimizing the control overhead.
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paper_title: An Attempt to Model the Human Body as a Communication Channel

paper_content:

Using the human body as a transmission medium for electrical signals offers novel data communication in biomedical monitoring systems. In this paper, galvanic coupling is presented as a promising approach for wireless intra-body communication between on-body sensors. The human body is characterized as a transmission medium for electrical current by means of numerical simulations and measurements. Properties of dedicated tissue layers and geometrical body variations are investigated, and different electrodes are compared. The new intra-body communication technology has shown its feasibility in clinical trials. Excellent transmission was achieved between locations on the thorax with a typical signal-to-noise ratio (SNR) of 20 dB while the attenuation increased along the extremities.
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paper_title: Characterization of ultra-wide bandwidth wireless indoor channels: a communication-theoretic view

paper_content:

An ultra-wide bandwidth (UWB) signal propagation experiment is performed in a typical modern laboratory/office building. The bandwidth of the signal used in this experiment is in excess of 1 GHz, which results in a differential path delay resolution of less than a nanosecond, without special processing. Based on the experimental results, a characterization of the propagation channel from a communications theoretic view point is described, and its implications for the design of a UWB radio receiver are presented. Robustness of the UWB signal to multipath fading is quantified through histograms and cumulative distributions. The all RAKE (ARAKE) receiver and maximum-energy-capture selective RAKE (SRAKE) receiver are introduced. The ARAKE receiver serves as the best case (bench mark) for RAKE receiver design and lower bounds the performance degradation caused by multipath. Multipath components of measured waveforms are detected using a maximum-likelihood detector. Energy capture as a function of the number of single-path signal correlators used in UWB SRAKE receiver provides a complexity versus performance tradeoff. Bit-error-probability performance of a UWB SRAKE receiver, based on measured channels, is given as a function of the signal-to-noise ratio and the number of correlators implemented in the receiver.
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paper_title: Theoretical Limits for Estimation of Vital Signal Parameters Using Impulse Radio UWB

paper_content:

In this paper, Cramer-Rao lower bounds (CRLBs) for estimation of vital signal parameters, such as respiration and heart-beat rates, using ultra-wideband (UWB) pulses are derived. In addition, a simple closed-form CRLB expression is obtained for sinusoidal displacement functions under certain conditions. Moreover, a two-step suboptimal solution is proposed, which is based on time-delay estimation via matched filtering followed by least-squares (LS) estimation. It is shown that the proposed solution is asymptotically optimal in the limit of certain system parameters. Simulation studies are performed to evaluate the lower bounds and performance of the proposed solution for realistic system parameters.
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paper_title: Development and performance analysis of an intra-body communication device

paper_content:

Personal area networks would benefit from a wireless communication system in which a variety of information could be exchanged through wearable electronic devices and sensors. Intra-body communication using the human body as the transmission medium enables wireless communication without transmitting radio waves through the air. A human arm phantom is designed and used to reduce uncertainty in experiments with the human body. The phantom exhibits transmission characteristics similar to those of the human body at frequencies between 1 MHz and 10 MHz. A 10.7 MHz frequency modulation (FM) intra-body transmitter and receiver are developed which allow transmission of analog sine waves even in the presence of external noise. Digital data transmission at 9600 bps was also achieved using newly fabricated 10.7 MHz frequency shift keying (FSK) transmitter and receiver devices. The carrier frequency of 10.7 MHz, which is the intermediate frequency in FM radio receivers, means that a wide selection of commercial radio frequency (RF) devices is available.
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paper_title: A 0.2-mW 2-Mb/s Digital Transceiver Based on Wideband Signaling for Human Body Communications

paper_content:

This paper presents a low-power wideband signaling (WBS) digital transceiver for data transmission through a human body for body area network applications. The low-power and highspeed human body communication (HBC) utilizes a digital transceiver chip based on WBS and adopts a direct-coupled interface (DCI) which uses an electrode of 50-Omega impedance. The channel investigation with the DCI identities an optimum channel bandwidth of 10 kHz to 100 MHz. The WBS digital transceiver exploits a direct digital transmitter and an all-digital clock and data recovery (CDR) circuit. To further reduce power consumption, the proposed CDR circuit incorporates a low-voltage digitally-controlled oscillator and a quadratic sampling technique. The WBS digital transceiver chip with a 0.25-mum standard CMOS technology has 2-Mb/s data rate at a bit error rate of 1.1 times 10-7, dissipating only 0.2 mW from a 1-V supply generated by a 1.5-V battery.
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paper_title: Coexistence of IEEE802.15.4 with other Systems in the 2.4 GHz-ISM-Band

paper_content:

Wireless systems continue to rapidly gain popularity. This is extremely true for data networks in the local and personal area, which are called WLAN and WPAN, respectively. However, most of those systems are working in the license-free industrial scientific medical (ISM) frequency bands, where neither resource planning nor bandwidth allocation can be guaranteed. To date, the most widespread systems in the 2.4 GHz ISM band are IEEE802.11 as stated in IEEE Std. 802-11 (1997) and Bluetooth, with ZigBee based in IEEE Std. 802.15.4 (2003) and IEEE802.15.4 as upcoming standards for short range wireless networks. In this paper we examine the mutual effects of these different communication standards. Measurements are performed with real-life equipment, in order to quantify coexistence issues
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paper_title: Experimental Studies on Human Body Communication Characteristics Based Upon Capacitive Coupling

paper_content:

Human Body Communication (HBC) is regarded as a burgeoning transmission technology for short-range body sensor network applications. However, there are currently few full-scale on-body measurements describing the principle of body channel propagation characteristics upon capacitive coupling. This paper focuses on the comprehensive experiments on different body parts to investigate body channel characteristics. Using capacitive coupling technique, the body channel characteristics were measured both in frequency domain and in time domain. Based on the whole body measurement results, it was found that the body maintained stable attenuation characteristics: the lowest attenuation is approximate-15dB at 28MHz. Arthrosis such as elbow, knee and wrist affected channel attenuation characteristic by about 2dB. Furthermore, the experiment results illustrate that the fat content in body also affects channel characteristic by 4dB.
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paper_title: A Novel Technique Enabling the Realisation of 60 GHz Body Area Networks

paper_content:

This paper presents a novel technique to enable over-body propagation at 60 GHz. A flexible material has been created that enables the propagation of surface waves around the body without the need of repeaters, high powers or high gain antennas. The solution is wireless and self-redundant, and will facilitate the development of light weight, high bandwidth, and low power, wireless body area networks that could offer improvements for mobile health monitoring applications as well as utility in sports and entertainment industries.
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paper_title: Characterization of On-Body Communication Channel and Energy Efficient Topology Design for Wireless Body Area Networks

paper_content:

Wireless body area networks (WBANs) offer many promising new applications in the area of remote health monitoring. An important element in the development of a WBAN is the characterization of the physical layer of the network, including an estimation of the delay spread and the path loss between two nodes on the body. This paper discusses the propagation channel between two half-wavelength dipoles at 2.45 GHz, placed near a human body and presents an application for cross-layer design in order to optimize the energy consumption of different topologies. Propagation measurements are performed on real humans in a multipath environment, considering different parts of the body separately. In addition, path loss has been numerically investigated with an anatomically correct model of the human body in free space using a 3-D electromagnetic solver. Path loss parameters and time-domain channel characteristics are extracted from the measurement and simulation data. A semi-empirical path loss model is presented for an antenna height above the body of 5 mm and antenna separations from 5 cm up to 40 cm. A time-domain analysis is performed and models are presented for the mean excess delay and the delay spread. As a cross-layer application, the proposed path loss models are used to evaluate the energy efficiency of single-hop and multihop network topologies.
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paper_title: Energy-Efficient TDMA-Based MAC Protocol for Wireless Body Area Networks

paper_content:

Body Area Networks (BAN) are a specific type of Network structure. They are spread over a very small area and their available power is heavily constrained. Hence it is useful to have gateway points in the network, such as nodes carried around the belt, that are less power constrained and can be used for network coordination. This network structure can result in very low transmission power/range for the sensors and effective TDMA timing control. This paper presents an energy-efficient MAC protocol for communication within the Wireless Body Area Network. The protocol takes advantage of the fixed nature of the Body Area Network to implement a TDMA strategy with very little communication overhead, long sleep times for the sensor transceivers and robustness to communication errors. The protocol is implemented on the Analog Devices ADF7020 RF transceivers.
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paper_title: Ultra Low Power Signal Oriented Approach for Wireless Health Monitoring

paper_content:

In recent years there is growing pressure on the medical sector to reduce costs while maintaining or even improving the quality of care. A potential solution to this problem is real time and/or remote patient monitoring by using mobile devices. To achieve this, medical sensors with wireless communication, computational and energy harvesting capabilities are networked on, or in, the human body forming what is commonly called a Wireless Body Area Network (WBAN). We present the implementation of a novel Wake Up Receiver (WUR) in the context of standardised wireless protocols, in a signal-oriented WBAN environment and present a novel protocol intended for wireless health monitoring (WhMAC). WhMAC is a TDMA-based protocol with very low power consumption. It utilises WBAN-specific features and a novel ultra low power wake up receiver technology, to achieve flexible and at the same time very low power wireless data transfer of physiological signals. As the main application is in the medical domain, or personal health monitoring, the protocol caters for different types of medical sensors. We define four sensor modes, in which the sensors can transmit data, depending on the sensor type and emergency level. A full power dissipation model is provided for the protocol, with individual hardware and application parameters. Finally, an example application shows the reduction in the power consumption for different data monitoring scenarios.
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paper_title: Numerical Analysis of CSMA/CA for Pattern-Based WBAN System

paper_content:

This paper presents a numerical analysis of a CSMA/CA mechanism for a pattern-based WBAN system. Several equations are derived to observe the number of back-off periods required by high, medium, and low traffic BAN Nodes (BNs). Numerical results show that CSMA/CA mechanism is not suitable for medium and low traffic BNs, and high traffic BNs encounter heavy collision problems. This suggests the use of a TDMA-based solution that can solve a range of problems including traffic heterogeneity and correlation problems.
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paper_title: Heartbeat driven medium access control for body sensor networks

paper_content:

H-MAC is a novel Time Division Multiple Access (TDMA) based MAC protocol designed for Body Sensor Networks (BSNs). It improves energy efficiency by exploiting human heartbeat rhythm information to perform time synchronization for TDMA. Heartbeat rhythm is inherent in every human body and can be detected in a variety of biosignals. Therefore, biosensors in BSNs can extract the heartbeat rhythm from their sensory data. Moreover, all the rhythms represented by peak sequences are naturally synchronized since they are driven by the same source, the heartbeat. By following the rhythm, wireless biosensors can achieve time synchronization without having to turn on their radio to receive periodic timing information from a central controller, so that energy cost for time synchronization can be completely avoided and the lifetime of network can be prolonged. An active synchronization recovery scheme is also developed, in which two resynchronization procedures are implemented. The algorithms are verified using real world data from MIT-BIH multi-parameter database MIMIC.
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paper_title: ATLAS: A Traffic Load Aware Sensor MAC Design for Collaborative Body Area Sensor Networks

paper_content:

In collaborative body sensor networks, namely wireless body area networks (WBANs), each of the physical sensor applications is used to collaboratively monitor the health status of the human body. The applications of WBANs comprise diverse and dynamic traffic loads such as very low-rate periodic monitoring (i.e., observation) data and high-rate traffic including event-triggered bursts. Therefore, in designing a medium access control (MAC) protocol for WBANs, energy conservation should be the primary concern during low-traffic periods, whereas a balance between satisfying high-throughput demand and efficient energy usage is necessary during high-traffic times. In this paper, we design a traffic load-aware innovative MAC solution for WBANs, called ATLAS. The design exploits the superframe structure of the IEEE 802.15.4 standard, and it adaptively uses the contention access period (CAP), contention free period (CFP) and inactive period (IP) of the superframe based on estimated traffic load, by applying a dynamic "wh" (whenever which is required) approach. Unlike earlier work, the proposed MAC design includes load estimation for network load-status awareness and a multi-hop communication pattern in order to prevent energy loss associated with long range transmission. Finally, ATLAS is evaluated through extensive simulations in ns-2 and the results demonstrate the effectiveness of the protocol.
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paper_title: An ultra-low-power medium access control protocol for body sensor network

paper_content:

In this paper, BSN-MAC, a medium access control (MAC) protocol designed for Body Sensor Networks (BSNs) is proposed. Due to the traffic coupling and sensor diversity characteristics of BSNs, common MAC protocols can not satisfy the unique requirements of the biomedical sensors in BSNs. BSN-MAC exploits the feedback information from the deployed sensors to form a closed-loop control of the MAC parameters. A control algorithm is proposed to enable the BSN coordinator to adjust parameters of the IEEE 802.15.4 superframe to achieve both energy efficiency and low latency on energy critical nodes. We evaluate the performance of BSN-MAC by comparing it with the IEEE 802.15.4 MAC protocol using energy efficiency as the primary metric.
---
paper_title: Energy-Efficiency Analysis of a Distributed Queuing Medium Access Control Protocol for Biomedical Wireless Sensor Networks in Saturation Conditions

paper_content:

The aging population and the high quality of life expectations in our society lead to the need of more efficient and affordable healthcare solutions. For this reason, this paper aims for the optimization of Medium Access Control (MAC) protocols for biomedical wireless sensor networks or wireless Body Sensor Networks (BSNs). The hereby presented schemes always have in mind the efficient management of channel resources and the overall minimization of sensors’ energy consumption in order to prolong sensors’ battery life. The fact that the IEEE 802.15.4 MAC does not fully satisfy BSN requirements highlights the need for the design of new scalable MAC solutions, which guarantee low-power consumption to the maximum number of body sensors in high density areas (i.e., in saturation conditions). In order to emphasize IEEE 802.15.4 MAC limitations, this article presents a detailed overview of this de facto standard for Wireless Sensor Networks (WSNs), which serves as a link for the introduction and initial description of our here proposed Distributed Queuing (DQ) MAC protocol for BSN scenarios. Within this framework, an extensive DQ MAC energy-consumption analysis in saturation conditions is presented to be able to evaluate its performance in relation to IEEE 802.5.4 MAC in highly dense BSNs. The obtained results show that the proposed scheme outperforms IEEE 802.15.4 MAC in average energy consumption per information bit, thus providing a better overall performance that scales appropriately to BSNs under high traffic conditions. These benefits are obtained by eliminating back-off periods and collisions in data packet transmissions, while minimizing the control overhead.
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paper_title: Probabilistic routing in on-body sensor networks with postural disconnections

paper_content:

This paper presents a novel store-and-forward packet routing algorithm for Wireless Body Area Networks (WBAN) with frequent postural partitioning. A prototype WBAN has been constructed for experimentally characterizing on-body topology disconnections in the presence of ultra short range radio links, unpredictable RF attenuation, and human postural mobility. A probabilistic packet routing protocol is then developed using a stochastic link cost, reflecting the body postural trends. The performance of the proposed protocol is evaluated experimentally and via simulation, and is compared with a generic probabilistic routing protocol and a specialized on-body packet flooding mechanism that provides the routing delay lower-bounds. It is shown that via successful modeling of the spatio-temporal locality of link disconnection patterns, the proposed algorithm can provide better routing delay performance compared to the existing probabilistic routing protocols in the literature.
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paper_title: Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue

paper_content:

A network of biosensors can be implanted in a human body for health monitoring, diagnostics, or as a prosthetic device. Biosensors can be organized into clusters where most of the communication takes place within the clusters, and long range transmissions to the base station are performed by the cluster leader to reduce the energy cost. In some applications, the tissues are sensitive to temperature increase and may be damaged by the heat resulting from normal operations and the recharging of sensor nodes. Our work is the first to consider rotating the cluster leadership to minimize the heating effects on human tissues. We explore the factors that lead to temperature increase, and the process for calculating the specific absorption rate (SAR) and temperature increase of implanted biosensors by using the finite-difference time-domain (FDTD) method. We improve performance by rotating the cluster leader based on the leadership history and the sensor locations. We propose a simplified scheme, temperature increase potential, to efficiently predict the temperature increase in tissues surrounding implanted sensors. Finally, a genetic algorithm is proposed to exploit the search for an optimal temperature increase sequence.
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paper_title: Temperature-aware routing for telemedicine applications in embedded biomedical sensor networks

paper_content:

Biomedical sensors, called invivo sensors, are implanted in human bodies, and cause some harmful effects on surrounding body tissues. Particularly, temperature rise of the invivo sensors is dangerous for surrounding tissues, and a high temperature may damage them from a long term monitoring. In this paper, we propose a thermal-aware routing algorithm, called least total-route-temperature (LTRT) protocol, in which nodes temperatures are converted into graph weights, and minimum temperature routes are obtained. Furthermore, we provide an extensive simulation evaluation for comparing several other related schemes. Simulation results show the advantages of the proposed scheme.
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paper_title: An energy-efficient configuration management for multi-hop wireless body area networks

paper_content:

In this paper, a heuristic adaptive routing algorithm for an energy-efficient configuration management has been suggested which can reduce energy consumption while guaranteeing QoS for the emergency data in wireless body area networks (WBANs). The priority and vicinity of the nodes are taken into account for the selection of reachable parent nodes, when the nodes are disconnected due to the mobile nature of human body. We derive a mathematical model and presented algorithm for maintaining balanced power consumption with guaranteed QoS. A simulation has been performed to evaluate the performance of the proposed heuristic algorithm.
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paper_title: A Robust Protocol Stack for Multi-hop Wireless Body Area Networks with Transmit Power Adaptation ∗

paper_content:

Wireless Body Area Networks (WBANs) have characteristic properties that should be considered for designing a proper network architecture. Movement of on-body sensors, low quality and time-variant wireless links, and the demand for a reliable and fast data transmission at low energy cost are some challenging issues in WBANs. Using ultra low power wireless transceivers to reduce power consumption causes a limited transmission range. This implies that a multi-hop protocol is a promising design choice. This paper proposes a multi-hop protocol for human body health monitoring. The protocol is robust against frequent changes of the network topology due to posture changes, and variation of wireless link quality. A technique for adapting the transmit power of sensor nodes at run-time allows to optimize power consumption while ensuring a reliable outgoing link for every node in the network and avoiding network disconnection.
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paper_title: Energy Efficient Thermal Aware Routing Algorithms for Embedded Biomedical Sensor Networks

paper_content:

One of the major applications of sensor networks in near future will be in the area of biomedical research. Implanted biosensor nodes are already being used for various medical applications. These in-vivo sensor networks collect different biometric data and communicate the data to the base station wirelessly. These sensor networks produce heat, as the nodes have to communicate among themselves wirelessly. The rise in temperature of the nodes due to communication should not be very high. A high temperature of the in-vivo nodes for a prolonged period might damage the surrounding tissues. In this paper, we propose a new routing algorithm that reduces the amount of heat produced in the network. In the simple form, the algorithm routes packets to the coolest neighbor without inducing routing loops. In the adaptive form, the algorithm uses mechanisms to adapt to topologies with low degree of connectivity and to switch to shortest path routing if a time threshold is exceeded. The proposed algorithm performs much better in terms of reducing the amount of heat produced, delay and power consumption compared to the shortest hop routing algorithm and a previously proposed Thermal Aware Routing Algorithm (TARA).
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paper_title: Adapting radio transmit power in wireless body area sensor networks

paper_content:

Emerging body-wearable devices for continuous health monitoring are severely energy constrained and yet required to offer high communication reliability under fluctuating channel conditions. This paper investigates the dynamic adaptation of radio transmit power as a means of addressing this challenge. Our contributions are three-fold: we present empirical evidence that wireless link quality in body area networks changes rapidly when patients move; fixed radio transmit power therefore leads to either high loss (when link quality is bad), or wasted energy (when link quality is good). This motivates dynamic transmit power control, and our second contribution characterises the off-line optimal transmit power control that minimises energy usage subject to lower-bounds on reliability. Though not suited to practical implementation, the optimal gives insight into the feasibility of adaptive power control for body area networks, and provides a benchmark against which practical strategies can be compared. Our third contribution is to develop simple and practical on-line schemes that trade-off reliability for energy savings by changing transmit power based on feedback information from the receiver. Our schemes offer on average 9--25% savings in energy compared to using maximum transmit power, with little sacrifice in reliability, and demonstrate adaptive transmission power control as an effective technique for extending the lifetime of wireless body area sensor networks.
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paper_title: Cross-Layer Support for Energy Efficient Routing in Wireless Sensor Networks

paper_content:

The Dynamic Source Routing (DSR) algorithm computes a new route when packet loss occurs. DSR does not have an in-built mechanism to determine whether the packet loss was the result of congestion or node failure causing DSR to compute a new route. This leads to inefficient energy utilization when DSR is used in wireless sensor networks. In this work, we exploit cross-layer optimization techniques that extend DSR to improve its routing energy efficiency by minimizing the frequency of recomputed routes. Our proposed approach enables DSR to initiate a route discovery only when link failure occurs. We conducted extensive simulations to evaluate the performance of our proposed cross-layer DSR routing protocol. The simulation results obtained with our extended DSR routing protocol show that the frequency with which new routes are recomputed is 50% lower compared with the traditional DSR protocol. This improvement is attributed to the fact that, with our proposed cross-layer DSR, we distinguish between congestion and link failure conditions, and new routes are recalculated only for the latter.
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paper_title: A Low-delay Protocol for Multihop Wireless Body Area Networks

paper_content:

Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.
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paper_title: An ultra-low-power medium access control protocol for body sensor network

paper_content:

In this paper, BSN-MAC, a medium access control (MAC) protocol designed for Body Sensor Networks (BSNs) is proposed. Due to the traffic coupling and sensor diversity characteristics of BSNs, common MAC protocols can not satisfy the unique requirements of the biomedical sensors in BSNs. BSN-MAC exploits the feedback information from the deployed sensors to form a closed-loop control of the MAC parameters. A control algorithm is proposed to enable the BSN coordinator to adjust parameters of the IEEE 802.15.4 superframe to achieve both energy efficiency and low latency on energy critical nodes. We evaluate the performance of BSN-MAC by comparing it with the IEEE 802.15.4 MAC protocol using energy efficiency as the primary metric.
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paper_title: Cross-layer optimization protocol for guaranteed data streaming over Wireless Body Area Networks

paper_content:

In this paper, we study the problem of routing, bandwidth and flow assignment in Wireless Body Area Networks (BANs). Our solution considers BAN for real-time data streaming applications, where the real-time nature of data streams is of critical importance for providing a useful and efficient sensorial feedback for the user while system lifetime should be maximized. Thus, bandwidth and energy efficiency of the communication between energy constrained sensor nodes must be carefully optimized. The proposed solution takes into account nodes' residual energy during the establishment of the routing paths and adaptively allocates bandwidth to the nodes in the network. We also formulate the joint routing tree construction and bandwidth allocation problem as an Integer Linear Program that maximizes the network utility while satisfying the QoS requirements. We compare the resulting performance of our protocol with the optimal solution, and show that it closes a considerable portion of the gap from the theoretical optimal solution.
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paper_title: MOFBAN: a Lightweight Modular Framework for Body Area Networks

paper_content:

The increasing use of wireless networks, the constant miniaturization of electrical devices and the growing interest for remote health monitoring has led to the development of wireless on-body networks or WBANs. The research on communication in this type of network is still at it's infancy. The first communication protocols are being proposed, but a general architecture that can be used to integrate the protocols easily is still lacking. However, such an architecture could trigger the development of new protocols and ease the use of WBANs. In this paper, we present a lightweight modular framework for body area networks (MOFBAN). A modular structure is used which allows for a higher flexibility and improved energy efficiency. The paper first investigates the challenges and requirements needed for sending messages in a WBAN. Further, we discuss how this framework can be used when designing new protocols by defining the different components of the framework.
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paper_title: A smart poultice with reconfigurable sensor array for wearable cardiac healthcare

paper_content:

A smart poultice with reconfigurable sensor array is implemented by using planar fashionable circuit board (P-FCB) technology for wearable cardiac healthcare. It contains 1) 25-electrode array either for vital signal sensing or for external data communication, 2) a thin flexible battery, 3) a fabric inductor, and 4) a fabric circuit board on which a low power silicon chip is directly integrated. Start/stop operation of the smart poultice is realized by inductive-coupled remote controller with 8b ID verification function and external low power data transaction is achieved by using duty cycled body channel communication.
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paper_title: Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location

paper_content:

This paper investigates two major issues in using a tri-axial accelerometer-embedded mobile phone for continuous activity monitoring, i.e. the difference in orientations and locations of the device. Two experiments with a total of ten test subjects performed six daily activities were conducted in this study: one with a device fixed on the waist in sixteen different orientations and another with three different device locations (i.e., shirt-pocket, trouser-pocket and waist) in two different device orientations. For handling with varying device orientations, a projection-based method for device coordinate system estimation has been proposed. Based on the dataset with sixteen different device orientations, the experimental results have illustrated that the proposed method is efficient for rectifying the acceleration signals into the same coordinate system, yielding significantly improved activity recognition accuracy. After signal transformation, the recognition results of signals acquired from different device locations are compared. The experimental results show that when the sensor is placed on different rigid body, different models are required for certain activities.
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paper_title: An Attachable ECG Sensor Bandage with Planar-Fashionable Circuit Board

paper_content:

An attachable ECG sensor adhesive bandage is implemented for continuous ECG monitoring system by using Planar-Fashionable Circuit Board (P-FCB) technology. The sensor patch improves convenience at low cost: it is composed of dry electrodes and an inductor directly screen printed on fabric, and the sensor chip is also directly wire bonded on fabric. The sensor patch is wirelessly powered to remove battery for safety. Dry electrodes minimize skin irritation to enable long term monitoring. The implemented sensor patch successfully demonstrates capturing of ECG signal while dissipating only 12uW power.
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paper_title: Analysis of the severity of dyskinesia in patients with Parkinson's disease via wearable sensors

paper_content:

The aim of this study is to identify movement characteristics associated with motor fluctuations in patients with Parkinson's disease by relying on wearable sensors. Improved methods of assessing longitudinal changes in Parkinson's disease would enable optimization of treatment and maximization of patient function. We used eight accelerometers on the upper and lower limbs to monitor patients while they performed a set of standardized motor tasks. A video of the subjects was used by an expert to assign clinical scores. We focused on a motor complication referred to as dyskinesia, which is observed in association with medication intake. The sensor data were processed to extract a feature set responsive to the motor fluctuations. To assess the ability of accelerometers to capture the motor fluctuation patterns, the feature space was visualized using PCA and Sammon's mapping. Clustering analysis revealed the existence of intermediate clusters that were observed when changes occurred in the severity of dyskinesia. We present quantitative evidence that these intermediate clusters are the result of the high sensitivity of the proposed technique to changes in the severity of dyskinesia observed during motor fluctuation cycles.
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paper_title: Respiratory Rate and Flow Waveform Estimation from Tri-axial Accelerometer Data

paper_content:

There is a strong medical need for continuous, unobstrusive respiratory monitoring, and many shortcomings to existing methods. Previous work shows that MEMS accelerometers worn on the torso can measure inclination changes due to breathing, from which a respiratory rate can be obtained. There has been limited validation of these methods. The problem of practical continuous monitoring, in which patient movement disrupts the measurements and the axis of interest changes, has also not been addressed. We demonstrate a method based on tri-axial accelerometer data from a wireless sensor device, which tracks the axis of rotation and obtains angular rates of breathing motion. The resulting rates are validated against gyroscope measurements and show high correlation to flow rate measurements using a nasal cannula. We use a movement detection method to classify periods in which the patient is static and breathing signals can be observed accurately. Within these periods we obtain a close match to cannula measurements, for both the flow rate waveform and derived respiratory rates, over multi-hour datasets obtained from wireless sensor devices on hospital patients. We discuss future directions for improvement and potential methods for estimating absolute airflow rate and tidal volume.
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paper_title: Intelligent Mobile Health Monitoring System (IMHMS)

paper_content:

Health monitoring is repeatedly mentioned as one of the main application areas for Pervasive computing. Mobile Health Care is the integration of mobile computing and health monitoring. It is the application of mobile computing technologies for improving communication among patients, physicians, and other health care workers. As mobile devices have become an inseparable part of our life it can integrate health care more seamlessly to our everyday life. It enables the delivery of accurate medical information anytime anywhere by means of mobile devices. Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight and intelligent bio-sensor nodes. These nodes, capable of sensing, processing, and communicating one or more vital signs, can be seamlessly integrated into wireless personal or body area networks for mobile health monitoring. In this paper we present Intelligent Mobile Health Monitoring System (IMHMS), which can provide medical feedback to the patients through mobile devices based on the biomedical and environmental data collected by deployed sensors.
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paper_title: A Wearable ECG Acquisition System With Compact Planar-Fashionable Circuit Board-Based Shirt

paper_content:

A wearable electrocardiogram (ECG) acquisition system implemented with planar-fashionable circuit board (P-FCB)-based shirt is presented. The proposed system removes cumbersome wires from conventional Holter monitor system for convenience. Dry electrodes screen-printed directly on fabric enables long-term monitoring without skin irritation. The ECG monitoring shirt exploits a monitoring chip with a group of electrodes around the body, and both the electrodes and the interconnection are implemented using P-FCB to enhance wearability and to lower production cost. The characteristics of P-FCB electrode are shown, and the prototype hardware is implemented to successfully verify the proposed concept.
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paper_title: Mobile sensor data collector using Android smartphone

paper_content:

In this paper, we present a system using an Android smartphone that collects, displays sensor data on the screen and streams to the central server simultaneously. Bluetooth and wireless Internet connections are used for data transmissions among the devices. Also, using Near Field Communication (NFC) technology, we have constructed a more efficient and convenient mechanism to achieve an automatic Bluetooth connection and application execution. This system is beneficial on body sensor networks (BSN) developed for medical healthcare applications. For demonstration purposes, an accelerometer, a temperature sensor and electrocardiography (ECG) signal data are used to perform the experiments. Raw sensor data are interpreted to either graphical or text notations to be presented on the smartphone and the central server. Furthermore, a Java-based central server application is used to demonstrate communication with the Android system for data storage and analysis.
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paper_title: Pervasive body sensor network: an approach to monitoring the post-operative surgical patient

paper_content:

Patients recovering from abdominal surgery are at risk of complications due to reduced mobility as a result of post-operative pain. The ability to pervasively monitor the recovery of this group of patients and identify those at risk of developing complications is therefore clinically desirable, which may result in an early intervention to prevent adverse outcomes. This paper describes the development and evaluation of a pervasive network of body sensors developed for monitoring the recovery of post-operative patients both in the hospital and homecare settings.
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paper_title: On the Road to a Textile Integrated Bioimpedance Early Warning System for Lung Edema

paper_content:

Early detection of lung edema for patients suffering from chronic heart disease improves the medical treatment and can avoid committal of the patient to an intensive care unit. Therefore, an early warning system monitoring the amount of fluid in the lungs by measuring trans-thoracic bioimpedance outside the body has been developed. The proposed system(TiBIS) consists of a textile integrated measurement module and a Personal Digital Assistant for signal processing and user interaction.
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paper_title: Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease

paper_content:

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.
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paper_title: Breathing Feedback System with Wearable Textile Sensors

paper_content:

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straight forward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician.
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paper_title: Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

paper_content:

The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize the human affective state given four physiological signals. We describe difficult issues unique to obtaining reliable affective data and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-to-day variations: The features of different emotions on the same day tend to cluster more tightly than do the features of the same emotion on different days. To handle the daily variations, we propose new features and algorithms and compare their performance. We find that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projection and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral.
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paper_title: Elderly Risk Assessment of Falls with BSN

paper_content:

Due to the natural aging process, the risks associated with falling can increase significantly. For the elderly, this usually marks a rapid deterioration of their health. While there are identified strategies that can be adopted to reduce the number of falls, it is still not possible to prevent all falls. Clinically, the Tinetti Gait and Balance Assessment has been widely used to assess the risk of falls in elderly by examining balance and gait. This paper presents our initial results of using an ear-worn BSN sensor to detect aspects of the Tinetti Gait and Balance Assessment to predict the risk of falls compared to a healthy control cohort. For this study, data was collected from a control cohort of 12 healthy volunteers and a cohort of 16 elderly fallers of varying degrees of risk. The results derived have shown that it is possible to directly detect some aspects of the Tinetti Gait and Balance Assessment and the Timed Up and Go test, demonstrating the potential value of using the platform for continuous assessment in a home environment.
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paper_title: Portable Preimpact Fall Detector With Inertial Sensors

paper_content:

Falls and the resulting hip fractures in the elderly are a major health and economic problem. The goal of this study was to investigate the feasibility of a portable preimpact fall detector in detecting impending falls before the body impacts on the ground. It was hypothesized that a single sensor with the appropriate kinematics measurements and detection algorithms, located near the body center of gravity, would be able to distinguish an in-progress and unrecoverable fall from nonfalling activities. The apparatus was tested in an array of daily nonfall activities of young (n = 10) and elderly (n = 14) subjects, and simulated fall activities of young subjects. A threshold detection method was used with the magnitude of inertial frame vertical velocity as the main variable to separate the nonfall and fall activities. The algorithm was able to detect all fall events at least 70 ms before the impact. With the threshold adapted to each individual subject, all falls were detected successfully, and no false alarms occurred. This portable preimpact fall detection apparatus will lead to the development of a new generation inflatable hip pad for preventing fall-related hip fractures.
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paper_title: A Wearable Airbag to Prevent Fall Injuries

paper_content:

We have developed a wearable airbag that incorporates a fall-detection system that uses both acceleration and angular velocity signals to trigger inflation of the airbag. The fall-detection algorithm was devised using a thresholding technique with an accelerometer and gyro sensor. Sixteen subjects mimicked falls, and their acceleration waveforms were monitored. Then, we developed a fall-detection algorithm that could detect signals 300 ms before the fall. This signal was used as a trigger to inflate the airbag to a capacity of 2.4 L. Although the proposed system can help to prevent fall-related injuries, further development is needed to miniaturize the inflation system.
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paper_title: Self-organising object networks using context zones for distributed activity recognition

paper_content:

Activity recognition has a high applicability scope in patient monitoring since it has the potential to observe patients' actions and recognise erratic behaviour. Our activity recognition architecture described in this paper is particularly suited for this task due to the fact that collaboration of constituent components, namely Object Networks, Activity Map and Activity Inference Engine create a flexible and scalable platform taking into consideration needs of individual users. We utilise information generated from sensors that observe user interaction with the objects in the environment and also information from body-worn sensors. This information is processed in a distributed manner through the object network hierarchy which we formally define. The object network has the effect of increasing the level of abstraction of information such that this high-level information is utilised by the Activity Inference Engine. This engine also takes into consideration information from the user's profiles in order to deduce the most probable activity and at the same time observe any erratic or potentially unsafe behaviour. We also present a scenario and show the results of our study.
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paper_title: Implementation of Context-Aware Distributed Sensor Network System for Managing Incontinence Among Patients with Dementia

paper_content:

Incontinence is highly prevalent in Patients with Dementia (PWD) due to a decline in their physical and mental abilities. Those PWD may lie in soiled diaper for prolonged periods if timely diaper change is not in place. Current manual care practices may not be able to immediately detect soiled diaper, although costly and labor intensive scheduled checks are performed. Delays in diaper change can cause serious social and medical issues. So, timely and effective continence management is important to potentially avoid the undesirable consequences. By developing assistive system leveraging on sensors, wireless sensor network, ambient intelligence and reminders, it is feasible to detect soiled diaper and remind the carers for timely intervention. With around the clock sensing, distributed monitoring and context-aware intervention, timely diaper change is possible anywhere anytime without wasting unnecessary care-giving resources and without causing annoyances to the elderly.
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paper_title: Swimming Stroke Kinematic Analysis with BSN

paper_content:

The recent maturity of body sensor networks has enabled a wide range of applications in sports, well-being and healthcare. In this paper, we hypothesise that a single unobtrusive head-worn inertial sensor can be used to infer certain biomotion details of specific swimming techniques. The sensor, weighing only seven grams is mounted on the swimmer's goggles, limiting the disturbance to a minimum. Features extracted from the recorded acceleration such as the pitch and roll angles allow to recognise the type of stroke, as well as basic biomotion indices. The system proposed represents a non-intrusive, practical deployment of wearable sensors for swimming performance monitoring.
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paper_title: A Framework for Golf Training Using Low-Cost Inertial Sensors

paper_content:

Body Sensor Networks are rapidly expanding to everyday applications due to recent advancements in Micro-Electro-Mechanical Systems (MEMS) sensing, wireless communication and power management technologies. We leverage these advancements to develop a framework for the use of MEMS inertial sensors as a low-cost putting coach for golf. Accurate putting requires substantial control and precision that is acquired via significant practice. Unfortunately, many golfers are not aware that they are practicing flawed mechanics. An electronic coach has the capability to point out these flawed movements before they become the norm. Our framework is the first step to an electronic coach and consists of a model for a putting swing, the design of a custom sensor platform and the implementation of signal processing functions to accurately estimate the trajectory of the golf club. Based upon our model we propose the use of sensor fusion algorithms to increase accuracy without increasing hardware demands. The accuracy of the system is experimentally evaluated using a controlled test platform.
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paper_title: Simple Barcode System Based on Inonogels for Real Time pH-Sweat Monitoring

paper_content:

This paper presents the fabrication, characterization and the performance of a wearable, robust, flexible and disposable barcode system based on novel ionic liquid polymer gels (ionogels) for monitoring in real time mode the pH of the sweat generated during an exercise period. Up to now sweat analysis has been carried out using awkward methods of collecting sweat followed by laboratory analysis. The approach presented here can provide immediate feedback regarding sweat composition. The great advantage of sweat analysis is the fact that it is a completely non-invasive means of analyzing the wearer’s physiological state and ensuring their health and well-being.
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paper_title: TENNISSENSE: A MULTI-SENSORY APPROACH TO PERFORMANCE ANALYSIS IN TENNIS

paper_content:

There is sufficient evidence in the current literature that the ability to accurately capture and model the accelerations, angular velocities and orientations involved in the tennis stroke could facilitate a major step forward in the application of biomechanics to tennis coaching (Tanabe & Ito, 2007; Gordon & Dapena, 2006). The TennisSense Project, run in collaboration with Tennis Ireland, aims to create the infrastructure required to digitally capture physical, tactical and physiological data from tennis players in order to assist in their coaching and improve performance. This study examined the potential for using Wireless Inertial Monitoring Units (WIMUs) to model the biomechanical aspects of the tennis stroke and for developing coaching tools that utilise this information.
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paper_title: Sound Based Heart Rate Monitoring for Wearable Systems

paper_content:

This paper presents an alternative approach for heart rate measurement. Instead of using an ECG sensor, the proposed design uses sound signals received from a microphone which does not require skin-contact. Specifically, the design uses an air conductive microphone and an efficient algorithm to estimate the heart beat parameters of the wearer. The estimates are obtained for different activities undertaken by the wearer. The activities studied include sitting, jumping, reading, laughing, singing, and coughing. Data are collected from researchers and students working in the laboratory, and in the presence of lung sound, and other environmental sounds and noise. Preliminary results show that, the method can be an effective alternative means of monitoring cardiac (heart) sounds in a natural environment.
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paper_title: An Assistive Body Sensor Network Glove for Speech- and Hearing-Impaired Disabilities

paper_content:

This paper presents a hand-gesture based interface for facilitating communication among speech- and hearing-impaired disabilities. In the system, a wireless sensor glove equipped with five flex sensors and a 3D accelerometer is used as the input device. By integrating the speech synthesizer onto an automatic gesture recognition system, user's hand gestures can be translated into sounds. In this study, we proposed a hierarchical gesture recognition framework based on the combined use of multivariate Gaussian distribution, bigram and a set of rules for model and feature set selection, deriving from a detailed analysis of misclassified gestures in the confusion matrix. To illustrate the practical use of the framework, a gesture recognition experiment has been conducted on American Sign Language (ASL) finger spelling gestures with two additional gestures representing space and full stop. The recognition model has been validated on the pangram "The quick brown fox jumps over the lazy dog.".
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paper_title: Implantation and Explantation of a Wireless Epiretinal Retina Implant Device: Observations during the EPIRET3 Prospective Clinical Trial

paper_content:

PURPOSE ::: Visual sensations in patients with blindness and retinal degenerations may be restored by electrical stimulation of retinal neurons with implantable microelectrode arrays. A prospective trial was initiated to evaluate the safety and efficacy of a wireless intraocular retinal implant (EPIRET3) in six volunteers with blindness and RP. ::: ::: ::: METHODS ::: The implant is a remotely controlled, fully intraocular wireless device consisting of a receiver and a stimulator module. The stimulator is placed on the retinal surface. Data and energy are transmitted via an inductive link from outside the eye to the implant. Surgery included removal of the lens, vitrectomy, and implantation of the EPIRET3 device through a corneal incision. The clinical outcome after implantation and explantation of the device was determined. The implant was removed after 4 weeks, according to the study protocol. ::: ::: ::: RESULTS ::: Implantation was successful in all six patients. While the anterior part was fixed with transscleral sutures, the stimulating foil was placed onto the posterior pole and fixed with retinal tacks. The implant was well tolerated, causing temporary moderate postoperative inflammation, whereas the position of the implant remained stable until surgical removal. In all cases explantation of the device was performed successfully. Adverse events were a sterile hypopyon effectively treated with steroids and antibiotics in one case and a retinal break in a second case during explantation requiring silicone oil surgery. ::: ::: ::: CONCLUSIONS ::: The EPIRET3 system can be successfully implanted and explanted in patients with blindness and RP. The surgical steps are feasible, and the postoperative follow-up disclosed an acceptable range of adverse events.
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paper_title: Novel communication services based on human body and environment interaction: applications inside trains and applications for handicapped people

paper_content:

We believe that near-field radio intra-body communications, wherein human body is used as the transmission medium, will be a very suitable solution for body area networks with many interesting applications towards a ubiquitous communication world. This paper presents some potential new applications of great usefulness for handicapped people. Moreover, we have also envisioned applications inside train coaches where the use of mobile phones is restricted in countries such as Japan. On the other hand, we present the system model designed to carry out these applications by using intra-body communications. Finally, in order to investigate the feasibility of this kind of communication, we analyze experimentally the characteristics of the intra-body propagation channels and we evaluate the performance of several digital modulation schemes
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Title: A Survey of Body Sensor Networks

Section 1: Sensors
Description 1: Describe various types of sensors and their roles in BSNs, summarizing their functionalities, key components, and practical applications.

Section 2: State-of-the-Art Research on BSN Status
Description 2: Discuss recent advancements and current research trends in BSN sensor technology, focusing on wearability, data processing, energy control, and fault diagnosis.

Section 3: Classification of Sensors
Description 3: Classify BSN sensors based on different criteria like type of signals measured, data transmission media, deployment positions, and automatic adjustment capabilities.

Section 4: Main Researched Sensors
Description 4: Provide an in-depth analysis of the most commonly used sensors in BSNs, including ECG sensors, accelerometers, pressure sensors, and respiration sensors, outlining their specific attributes and use cases.

Section 5: Design of Sensor Nodes
Description 5: Discuss the design considerations of sensor nodes, focusing on energy control, fault diagnosis, and the reduction of sensor nodes to enhance system efficiency and performance.

Section 6: Trends and Challenges
Description 6: Analyze the development trends and technical challenges in BSN sensor research, covering aspects like power consumption, wearability, accuracy, and new material usage.

Section 7: Data Fusion
Description 7: Explain the process of data fusion in BSNs, including techniques for preprocessing, feature extraction, data fusion computing, and data compression, as well as state-of-the-art research in these areas.

Section 8: State-of-the-Art Research on Data Fusion
Description 8: Review major research achievements and latest developments in data fusion techniques applied within BSNs, with examples of prominent methods and algorithms.

Section 9: Network Communication
Description 9: Discuss BSN network communication, addressing network architecture, communication protocols, energy control, security issues, and comparison with WSNs.

Section 10: State-of-the-Art Research on Network Communication
Description 10: Review the latest research on BSN network communication, covering aspects like network topology, physical layer, MAC layer, and routing layer design factors.

Section 11: Applications of BSNs
Description 11: Explore various applications of BSNs across different fields such as medicine, social welfare, sports, and man-machine interfaces, outlining specific use cases and examples.

Section 12: Conclusions
Description 12: Summarize the key points covered in the paper, emphasizing the current status, challenges, and future directions of BSN research and applications.