Power constrained sensor sample selection for improved form factor and lifetime in localized BANs

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.