Hierarchical Conditional Random Fields for GPS-based Activity Recognition

<p>Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person's activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent model of a person's activities and places. In contrast to existing techniques, our approach takes high-level context into account in order to detect the significant locations of a person. Our experiments show significant improvements over existing techniques. Furthermore, they indicate that our system is able to robustly estimate a person's activities using a model that is trained from data collected by other persons.</p>

http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/places-isrr-... PDF