Ultra Low-Power Signal Processing in Wearable Monitoring Systems: A Tiered Screening Architecture with Optimal Bit Resolution
Advances in technology have led to the development of wearable sensing, computing, and communication devices that can be woven into the physical environment of our daily lives, enabling a large variety of new applications in several domains, including wellness and health care. Despite their tremendous potential to impact our lives, wearable health monitoring systems face a number of hurdles to become a reality. The enabling processors and architectures demand a large amount of energy, requiring sizable batteries. In this article, we propose a granular decision-making architecture for physical movement monitoring applications. The module can be viewed as a tiered wake-up circuitry. This decision-making module, in combination with a low-power microcontroller, allows for significant power saving through an ultra low-power processing architecture. The significant power saving is achieved by performing a preliminary ultra low-power signal processing, and hence, keeping the microcontroller off when the incoming signal is not of interest. The preliminary signal processing is performed by a set of special-purpose functional units, also called screening blocks, that implement template matching functions. We formulate and solve an optimization problem for selecting screening blocks such that the accuracy requirements of the signal processing are accommodated while the total power is minimized. Our experimental results on real data from wearable motion sensors show that the proposed algorithm achieves 63.2% energy saving while maintaining a sensitivity of 94.3% in recognizing transitional actions.
signal processing wearable monitoring systems performance algorithms design medical embedded systems body sensor networks power optimization