Activity recognition via user-trace segmentation

A major issue of activity recognition in sensor networks is automatically recognizing a user's high-level goals accurately from low-level sensor data. Traditionally, solutions to this problem involve the use of a location-based sensor model that predicts the physical locations of a user from the sensor data. This sensor model is often trained offline, incurring a large amount of calibration effort. In this article, we address the problem using a goal-based segmentation approach, in which we automatically segment the low-level user traces that are obtained cheaply by collecting the signal sequences as a user moves in wireless environments. From the traces we discover primitive signal segments that can be used for building a probabilistic activity model to recognize goals directly. A major advantage of our algorithm is that it can reduce a significant amount of human effort in calibrating the sensor data while still achieving comparable recognition accuracy. We present our theoretical framework for activity recognition, and demonstrate the effectiveness of our new approach using the data collected in an indoor wireless environment.