XPro

Abstract

Wearable computing systems have spurred many opportunities to continuously monitor human bodies with sensors worn on or implanted in the body. These emerging platforms have started to revolutionize many fields, including healthcare and wellness applications, particularly when integrated with intelligent analytic capabilities. However, a significant challenge that computer architects are facing is how to embed sophisticated analytic capabilities in wearable computers in an energy-efficient way while not compromising system performance. In this paper, we present XPro, a novel cross-end analytic engine architecture for wearable computing systems. The proposed cross-end architecture is able to realize a generic classification design across wearable sensors and a data aggregator with high energy-efficiency. To facilitate the practical use of XPro, we also develop an Automatic XPro Generator that formally generates XPro instances according to specific design constraints. As a proof of concept, we study the design and implementation of XPro with six different health applications. Evaluation results show that, compared with state-of-the-art methods, XPro can increase the battery life of the sensor node by 1.6-2.4X while at the same time reducing system delay by 15.6-60.8% for wearable computing systems.