Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Worst and Best-Case Coverage in Sensor Networks
IEEE Transactions on Mobile Computing
Action coverage formulation for power optimization in body sensor networks
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Energy comparison and optimization of wireless body-area network technologies
Proceedings of the ICST 2nd international conference on Body area networks
The SmartCane system: an assistive device for geriatrics
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Energy optimization in wireless medical systems using physiological behavior
WH '10 Wireless Health 2010
Longitudinal high-fidelity gait analysis with wireless inertial body sensors
WH '10 Wireless Health 2010
Empath: a continuous remote emotional health monitoring system for depressive illness
Proceedings of the 2nd Conference on Wireless Health
Resource-efficient and reliable long term wireless monitoring of the photoplethysmographic signal
Proceedings of the 2nd Conference on Wireless Health
Driving low-power wearable systems with an adaptively-controlled foot-strike scavenging platform
Proceedings of the 2013 International Symposium on Wearable Computers
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Wearable sensing systems are paving the way for significant advances in diagnosis, preventative healthcare and tele-healthcare, by facilitating a variety of wireless health applications for medical signal and diagnostic monitoring and assessment. However, the considerable spatial and temporal sampling for multiple sensed modalities that enable these applications, also makes them power hungry, requiring large, heavy power supplies, and leading to a tradeoff between usability and lifetime. We propose a sampling algorithm to overcome this trade-off by capitalizing on the spatio-temporal redundancy inherent to Body Area Networks owing to their localized nature, as well as, assessing sample relevance based on its contribution to the predicted diagnostic(s). Our approach improves energy-efficiency and raises contextual sample quality, by tackling sample selection simultaneously in the spatial and temporal domains, yielding improved diagnostic accuracy under power-constraints. We present our algorithm in the context of diagnostics gleaned from a foot plantar pressure measurement platform and illustrate its efficacy based on real datasets collected by the platform.