Hierarchical Conditional
Random Fields for GPS-based Activity
Recognition
Proc. of the International Symposium
of Robotis Research (ISRR 2005).
Abstract
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.