Advances in Neural Information Processing Systems
19 (NIPS-05)
2005.
Abstract
Learning patterns of human behavior from sensor data is extremely
important for high-level activity inference. We show how to extract
and label a person's activities and significant places from traces
of GPS data. In contrast to existing techniques, our approach
simultaneously detects and classifies the significant locations of a
person and takes the high-level context into account. Our system
uses relational Markov networks to represent the hierarchical
activity model that encodes the complex relations among GPS
readings, activities and significant places. We apply FFT-based
message passing to perform efficient summation over large numbers of
nodes in the networks. We present experiments that show significant
improvements over existing techniques.