Uttam K. Sengupta

Trading off prediction accuracy and power consumption for context-aware wearable computing

Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the -wearable systems energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy.

Device ensembles [ubiquitous computing]

The ubiquity of wireless local and personal area networks, an expanding array of intelligent handheld devices, and an increasingly mobile lifestyle are enabling new forms of ensemble computing. Looking across four system layers - link, network, data, and application - the authors survey the technologies, standards, and leading-edge research making device ensembles a reality.