Activity Recognition from Wearable Sensors

Recent advances in wearable sensing and computing devices and in fast, probabilistic inference techniques make possible the fine-grained estimation of a person's activities over extended periods of time.  In this talk I will show how dynamic Bayesian networks and conditional random fields can be used to estimate the location and activity of a person based on information such as GPS readings or WiFi signal strength.  Our models use multiple levels of abstraction to bridge the gap between raw sensor measurements and high level information such as a user's mode of transportation, her current goal, and her significant places (e.g. home or work place).  I will also present work on using RFID tags or a wearable multi-sensor system to estimate a person's fine-grained activities.

This is joint work with Brian Ferris, Lin Liao, Don Patterson, Amarnag Subramanya, Jeff Bilmes, Gaetano Borriello, and Henry Kautz.

Speaker Bio

Dieter Fox

Dieter Fox is Associate Professor and Director of the Robotics and State Estimation Lab in the Computer Science & Engineering Department at the University of Washington, Seattle. He obtained his Ph.D. from the University of Bonn, Germany.  Before joining UW, he spent two years as a postdoctoral researcher at the CMU Robot Learning Lab.

Dieter's research focuses on probabilistic state estimation with applications in robotics and activity recognition.