Carer: Efficient Dynamic Sensing For Continuous Activity Monitoring

2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)(2011)

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Abstract
Advancement in wireless health sensor systems has triggered rapidly expanding research in continuous activity monitoring for chronic disease management or promotion and assessment of physical rehabilitation. Wireless motion sensing is increasingly important in treatments where remote collection of sensor measurements can provide an in-field objective evaluation of physical activity patterns. The well-known challenge of limited operating lifetime of energy-constrained wireless health sensor systems continues to present a primary limitation for these applications. This paper introduces CARER, a software system that supports a novel algorithm that exploits knowledge of context and dynamically schedules sensor measurement episodes within an energy consumption budget while ensuring classification accuracy. The sensor selection algorithm in the CARER system is based on Partially Observable Markov Decision Process (POMDP). The parameters for the POMDP algorithm can be obtained through standard maximum likelihood estimation. Sensor data are also collected from multiple locations of the subjects body, providing estimation of an individual's daily activity patterns.
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Key words
dynamic scheduling,maximum likelihood estimate,wireless sensor networks,bluetooth,software systems,accuracy,markov processes,physical activity,sensors,maximum likelihood estimation,heuristic algorithm,patient monitoring,sensitivity
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