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 …