Henry Kautz: Understanding Human Behaviour from Sensor Data

Categories: Event

The convergence of advances in algorithms for probabilistic reasoning and the development of low-cost, easily-deployed sensors is reviving the dream of AI to develop systems that can understand the narrative of ordinary human life. On the reasoning side, the AI community is developing techniques that bridge the gap between propositional Bayesian representations and hierarchical models of goals, plans, and actions. On the sensing side, new technologies such as RFID tags, GPS, motes, and wearable multi-modal sensors allow us to gather direct information about many aspects of human experience. I will describe recent work with my students and colleagues on developing systems that learn patterns of human activity for everyday tasks, both indoors and outdoors, using a variety of dynamic probabilistic models. I will then describe applications of these techniques to healthcare systems as part of the Assisted Cognition Project, a joint effort between our departments of computer science and rehabilitation medicine.

More information: http://www.cs.ubc.ca/events/seminars/csicics.shtml


Thursday, March 16, 2006 - 15:00 to 16:30


DMP 310



When: to

Where: DMP 310 - 6245 Agronomy Rd, Vancouver, BC, V6T 1Z4

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