CyHOP: A generic framework for real-time power-performance optimization in networked wearable motion sensors

Abstract

Power consumption is a major obstacle in designing stringent resource constraint wearables. Several system-level design considerations contribute to energy consumption of these systems which must be taken into account while designing the system. We propose a power-performance optimization framework, namely CyHOP (Cyclic and Holistic Optimization framework), for connected wearable motion sensors. While existing work focus solely on one design parameter, our approach globally trades-off the performance of activity recognition and power consumption. CyHOP is capable of optimally adjusting the system to fulfill specific application needs. Using a smoothing technique, the initial multi-variate non-convex optimization problem is reduced to a convex problem and solved using our devised derivative-free optimization approach, namely, cyclic coordinate search. Our model performs a linear search by cycling through the system variables on each iteration until it converges to the global optimum. Using real-world data collected with wearable motion sensors during activity monitoring, we validate our approached with various performance thresholds ranging from 40% to 80%.