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We present a novel sampling method to overcome the tradeoff between sensing fidelity and energy-efficiency in the context of localized sensor arrays used by Body Area Networks (BANs). Prior research has tackled this tradeoff as a coverage problem, wherein a subset of sensors must cover the sensor field. Instead, we formulate it as a power-constrained sampling problem, limiting the number of samples taken per epoch to produce schedules with enhanced coverage and energy savings. This formulation capitalizes on the periodic nature and the strong spatio-temporal interactions that are innate to BAN sensor samples. Our algorithm produces schedules with over 170% in energy savings with increased sensor coverage that yields up to a 41% improvement in diagnostic estimates.