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Power consumption is identified as one of the main complications in designing practical wearable systems, mainly due to their stringent resource limitations. When designing wearable technologies, several system-level design choices, which directly contribute to the energy consumption of these systems, must be considered. In this article, we propose a computationally lightweight system optimization framework that trades off power consumption and performance in connected wearable motion sensors. While existing approaches exclusively focus on one or a few hand-picked design variables, our framework holistically finds the optimal power-performance solution with respect to the specified application need. Our design tackles a multi-variant non-convex optimization problem that is theoretically hard to solve. To decrease the complexity, we propose a smoothing function that reduces this optimization to a convex problem. The reduced optimization is then solved in linear time using a devised derivative-free optimization approach, namely cyclic coordinate search. We evaluate our framework against several holistic optimization baselines using a real-world wearable activity recognition dataset. We minimize the energy consumption for various activity-recognition performance thresholds ranging from 40% to 80% and demonstrate up to 64% energy savings.

Trading Off Power Consumption and Prediction Performance in Wearable Motion Sensors