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 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.