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Recent advances in the scope of wearable devices and networks make body area sensor networks (BASNs) an extremely attractive tool to the fields of mobile and tele-health, owing to the range of medical applications they can serve and the diagnostic richness of patient data they can offer. However, for BASNs to achieve true ubiquity, they must be scalable in their support of automated patient data collection, making usability and reliability key considerations. Its designers must wrestle with the tradeoff between usability, hindered by device intrusiveness into the behaviors it measures, and lifetime, enhanced by large power supplies and expensive, sturdy components. Furthermore, the validity and reliability of the collected data are paramount. In this paper, we consider these issues in the context of localized multi-sensory wearable networks and present a method to generate low-power sampling schedules that are resilient to sensor faults while achieving high diagnostic fidelity. We jointly formulate this as a power-constrained sampling problem wherein the number of sensors sampled per epoch are limited, and, a fault tolerant scheduling problem wherein the sampling scheme offers enough redundancy to endure up to a predefined number of sensor faults while maintaining diagnostic accuracy. This formulation is based on, 1) the localized scope of BASNs that engenders strong spatio-temporal interactions in the samples, and, 2) the periodic nature of human behaviors measured. We present our algorithm in the context of gait diagnostics derived from a foot plantar pressure measurement platform and illustrate its performance based on real datasets collected by it.