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Body Sensor Networks (BSN), 2011 International Conference on

Date 23-25 May 2011

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Displaying Results 1 - 25 of 48
  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • [Title page i]

    Publication Year: 2011 , Page(s): i
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  • [Title page iii]

    Publication Year: 2011 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011 , Page(s): iv
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  • Table of contents

    Publication Year: 2011 , Page(s): v - viii
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  • Welcome message from the BSN 2011 General Chair

    Publication Year: 2011 , Page(s): ix
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  • Message from the Technical Program Chairs

    Publication Year: 2011 , Page(s): x
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  • BSN 2011 Committees

    Publication Year: 2011 , Page(s): xi
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  • Technical Program Committee

    Publication Year: 2011 , Page(s): xii
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  • GeM-REM: Generative Model-Driven Resource Efficient ECG Monitoring in Body Sensor Networks

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (390 KB) |  | HTML iconHTML  

    With recent advances in smart phones and wearable sensors, Body Sensor Networks (BSNs) have been proposed for use in continuous, remote electrocardiogram (ECG) monitoring. In such systems, sampling the ECG at clinically recommended rates (250 Hz) and wireless transmission of the collected data incurs high energy consumption at the energy-constrained body sensor. The large volume of collected data also makes data storage at the sensor infeasible. Thus, there is a need for reducing the energy consumption and data size at the sensor, while maintaining the ECG quality required for diagnosis. In this paper, we propose GeM-REM, a resource-efficient ECG monitoring method for BSNs. GeM-REM uses a generative ECG model at the base station and its lightweight version at the sensor. The sensor transmits data only when the sensed ECG deviates from model-based values, thus saving transmission energy. Further, the model parameters are continually updated based on the sensed ECG. The proposed approach enables storage of ECG data in terms of model parameters rather than data samples, which reduces the required storage space. Implementation on a sensor platform and evaluation using real ECG data from MIT-BIH dataset shows transmission energy and data storage reduction ratios of 42.1:1 and 37.3:1 respectively, which are better than state of the art ECG data compression schemes. View full abstract»

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  • An Assistive Body Sensor Network Glove for Speech- and Hearing-Impaired Disabilities

    Publication Year: 2011 , Page(s): 7 - 12
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1261 KB) |  | HTML iconHTML  

    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.". View full abstract»

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  • Human Back Movement Analysis Using BSN

    Publication Year: 2011 , Page(s): 13 - 18
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB) |  | HTML iconHTML  

    Human back movement estimation is clinically important for assessing patients with back pain. Most current techniques are limited to simple spinal movement angles without consideration of surrounding muscle movement and backplane rotation and torsion. These three dimensional analysis is fraught with difficulties due to the complex nature of the movement and sensor placement. In this paper, a consistent method based on multiple Body Sensor Network (BSN) nodes for the measurement of 3D bending and twist of the back is proposed. In our method, five BSN nodes, each consisting of a three axis accelerometer, a gyroscope and a magnetometer, are placed at the human back. Euler angles are then defined to represent the orientation for human back segments, kinematics analysis is then derived. An unscented Kalman filter (UKF) is deployed to estimate the defined Euler angles. Detailed experimental results have shown the feasibility and effectiveness of the proposed measurement and analysis framework. View full abstract»

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  • Power Aware Wireless Data Collection for BSN Data Repositories

    Publication Year: 2011 , Page(s): 19 - 21
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (206 KB) |  | HTML iconHTML  

    Wearable sensor nodes are highly constrained in terms of size, and, as a result, battery size and capacity. During a real time data collection, sensor nodes can communicate data continuously, however, this may reduce the system lifetime. Hence, we suggest an intelligent data collection algorithm that screens the sensor data and transmits only the segments of sensor data that might be of interest. Additionally, the proposed approach does not require extensive system training, since it is based on a flexible Body Sensor Network(BSN) data repository. User can select movements of interest in a repository, and load the sensor nodes with the relevant meta data. Based on the meta data, sensor nodes can decide whether a specific part of the collected sensor data needs to be transmitted. We extend the idea by exploring the idea of dynamically associating the relevance of the data to the amount of energy available at the sensor node. View full abstract»

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  • Low Power Tiered Wake-up Module for Lightweight Embedded Systems Using Cross Correlation

    Publication Year: 2011 , Page(s): 22 - 24
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (221 KB) |  | HTML iconHTML  

    A major objective in design of wearable and light-weight embedded systems is reducing the power consumption. This leads to reduction of the battery size and enhances the wear ability of the system. In this paper, we propose an ultra low power tiered wake-up architecture with signal processing capability. The signal processing is based on template matching and normalized cross correlation. The template matching at the beginning is performed with low sensitivity (with fewer bits and samples) but at very low power. Initial template matching removes signals that are obviously not of interest. If the signal is likely to be of interest, the sensitivity and the power consumption of the template matching blocks are gradually increased, until the signal of interest is detected with a reasonable confidence. Consequently, a microcontroller is activated for additional processing. The tunable parameters for template matching include the number of samples, the size of template (window size) and the number of bits per sample. The proposed architecture can enable the next generation of ultra low power or even battery less wearable and implantable computers due to tremendously reducing the power consumption of the signal processing. We estimate that the power consumption of the proposed tiered wake-up circuitry will be three to six orders of magnitude smaller than state-of-the-art low power microcontrollers, depending on the complexity of the template matching. Further, the proposed architecture provides high level of programmability which is lacking in ASIC architectures custom built for applications. View full abstract»

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  • Signal Regeneration and Function Rebuilding Using Microelectronic Neural Bridge between Two Far-Separated Nervous Systems

    Publication Year: 2011 , Page(s): 25 - 28
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (374 KB) |  | HTML iconHTML  

    This paper reviews at first the features of the present information techniques including the telephone, the television, the computer, and the body sensor network briefly. Then, the concept and the construction of microelectronic neural bridges (MENB) are discussed. A special animal experiment in which the signal regeneration and the function rebuilding were realized by using a MENB between two far-separated nervous systems is demonstrated. The applications of presented concept are prospected. View full abstract»

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  • Observing Recovery from Knee-Replacement Surgery by Using Wearable Sensors

    Publication Year: 2011 , Page(s): 29 - 34
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (339 KB) |  | HTML iconHTML  

    A progressive improvement in gait following knee arthroplasty surgery can be observed during walking and transitional activities such as sitting/standing. Accurate assessment of such changes traditionally requires the use of a gait lab, which is often impractical, expensive, and labour intensive. Quantifying gait impairment following knee arthroplasty by employing wearable sensors allows for continuous monitoring of recovery. This study employed a recognised protocol of activities both pre-operatively, and at regular intervals up to twenty-four weeks post-total knee arthroplasty. The results suggest that a wearable miniaturised ear-worn sensor is potentially useful in monitoring post-operative recovery, and in identifying patients who fail to improve as expected, thus facilitating early clinical review and intervention. View full abstract»

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  • Development of a Wearable Sensor System for Measuring Body Joint Flexion

    Publication Year: 2011 , Page(s): 35 - 40
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    This paper presents a novel approach for measuring and monitoring human body joint angles using wearable sensors. This type of monitoring is beneficial for therapists and physicians as it allows them to assess patients' activities remotely. In our approach multiple flex-sensors are mounted on supportive cloth to measure the flexion of a joint. The changes in the resistivity of the flex-sensors are measured using an electronic board. We utilize an Extended Kalman Filter (EKF) to predict the joint angle based on the dynamic model of the joint movement and the measurements obtained from the flex-sensors. Due to variations in the measured angle by each sensor, the outputs are fussed to reduce the error and estimate the best value for the actual body joint angle. We evaluated the effectiveness and performance of our approach for measuring knee joint angle by comparing with the measured angles using goniometer. The result shows that the average of error is 6.92Ë with correlation of 0.98. View full abstract»

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  • Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location

    Publication Year: 2011 , Page(s): 41 - 46
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (944 KB) |  | HTML iconHTML  

    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. View full abstract»

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  • A Multi-technology Approach to Identifying the Reasons for Lateral Drift in Professional and Recreational Darts

    Publication Year: 2011 , Page(s): 47 - 52
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (495 KB) |  | HTML iconHTML  

    This work performs an extensive charterisation of precision targeted throwing in professional and recreational darts. The goal is to identify the contributing factors for lateral drift or throwing inaccuracy in the horizontal plane. A multi technology approach is adopted whereby a custom built body area network of wireless inertial measurement devices monitor tilt, force and timing, an optical 3D motion capture system provides a complete kinematic model of the subject, electromyography sensors monitor muscle activation patterns and a force plate and pressure mat capture tactile pressure and force measurements. The study introduces the concept of constant throwing rhythm and highlights how landing errors in the horizontal plane can be attributable to a number of variations in arm force and speed, centre of gravity and the movements of some of the bodies non throw related extremities. View full abstract»

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  • Compressive Sensing of Neural Action Potentials Using a Learned Union of Supports

    Publication Year: 2011 , Page(s): 53 - 58
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (750 KB) |  | HTML iconHTML  

    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. View full abstract»

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  • Methods to Characterize Sensors for Capturing Body Sounds

    Publication Year: 2011 , Page(s): 59 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2402 KB) |  | HTML iconHTML  

    Ambulatory monitoring of body sounds is still hindered today by a lack of systematic optimization of sensor, sensor packaging and mounting conditions. This paper presents generic, objective and application-independent evaluation methods that can be used for quantitative on-body sensor optimization. The methods are applied on the evaluation of a thin, bendable and ultra low-power piezoelectric sensor to capture body sounds. The results of this study show that both chest expansion and the pitch of speech can be determined reliably with this sensor, even under conditions of walking, whereas heart sounds can be captured only under quiet, motionless conditions. A low-pass filtering effect of the sensor assembly with a very low cut-off frequency made further investigation of lung sounds impossible. The presented methods can be used in future studies to optimize sensor, sensor packaging and mounting conditions. View full abstract»

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  • Robust Hierarchical System for Classification of Complex Human Mobility Characteristics in the Presence of Neurological Disorders

    Publication Year: 2011 , Page(s): 65 - 70
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1958 KB) |  | HTML iconHTML  

    Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of inter- fering signals induced by gait changes. Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at View full abstract»

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  • Extracting Spatio-Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation

    Publication Year: 2011 , Page(s): 71 - 76
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (660 KB) |  | HTML iconHTML  

    The fidelity of many inertial Body Sensor Network (BSN) applications depends on accurate spatio-temporal information retrieved from body-worn devices. However, there are many challenges caused by inherent sensor errors in inertial BSNs and the uncertainty of dynamic human motion in various situations, such as integration drift and mounting error. Spatial information is especially difficult to extract from inertial data. This paper presents practical methods to minimize errors caused by these challenges within the context of a case study -- gait speed estimation - where both temporal and spatial information are crucial for accuracy. These methods include a practical calibration procedure for correcting mounting error in order to obtain more accurate spatial information and a refined human gait model for more accurate temporal information. View full abstract»

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  • Learning a Policy for Coordinated Sampling in Body Sensor Networks

    Publication Year: 2011 , Page(s): 77 - 82
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (379 KB) |  | HTML iconHTML  

    This paper describes a method for learning coordination policies in body sensor networks. The learning of a compact coordination policy is important for implementing the policy in sensor nodes with limited memory. We present a novel algorithm, Reinforcement Learning Average Approximation (RLAA), to learn local coordination policies for each sensor node from globally joint rewards. These local policies are obtained by reinforcement learning and averaging state-action tables under a stochastic process model. We show results on a simulation of an existing body sensor network interfaced with transdermal sensors that demonstrate the performance of this learning scheme. Experimental results show that the performance of the RLAA algorithm is significantly better than a random policy and is close to the optimal policy that can be obtained from solving a global Markov Decision Process while the learning step is fast. The results also show that the RLAA algorithm is scalable to networks represented by large state spaces (in terms of number s of sensors and degree of discretization). View full abstract»

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  • A Low Power Wake-Up Circuitry Based on Dynamic Time Warping for Body Sensor Networks

    Publication Year: 2011 , Page(s): 83 - 88
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (754 KB) |  | HTML iconHTML  

    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. View full abstract»

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