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.
CITATION STYLE
Liao, L., Fox, D., & Kautz, H. (2007). Hierarchical conditional random fields for GPS-based activity recognition. Springer Tracts in Advanced Robotics, 28. https://doi.org/10.1007/978-3-540-48113-3_41
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