Detecting activities from body-worn accelerometers via instance-based algorithms

The automatic and unobtrusive identification of user activities is one of the most challenging goals of context-aware computing. This paper discusses and experimentally evaluates instance-based algorithms to infer user activities on the basis of data acquired from body-worn accelerometer sensors. We show that instance-based algorithms can classify simple and specific activities with high accuracy. In addition, due to their low requirements, we show how they can be implemented on severely resource-constrained devices. Finally, we propose mechanisms to take advantage of the temporal dimension of the signal, and to identify novel activities at run time. (C) 2010 Elsevier B.V. All rights reserved.