Inference Techniques in Sensor-Based Activity Recognition
by
Alvin Raj
The prospect of ubiquitous computing applications being able to
automatically provide context-specific services makes
the task of extracting a person's activity from sensor data an important
goal. Data from a GPS unit and a shoulder
mounted multi-sensor-board which contain audio, accelerometer, light,
barometric, and humidity sensors, are used to
give the best estimate of a person's environment (e.g. outside, inside a
building, or inside a vehicle), and a
person's activity (e.g. stopped, walking, running, going
upstairs/downstairs, or driving).
Our research focuses on using graphical models to estimate the behavior of
the person, while imposing temporal
constraints on the state. I shall discuss two approaches used in solving
this problem. The first models the problem
as a variant of a Hidden Markov Model (HMM) and was implemented using the
Graphical Modeling Toolkit (GMTK). The
second approach uses Rao-Blackwellised particle filters with attached
hierarchical HMMs to obtain a joint estimate of
position and activity.
Advised by Dieter Fox
CSE 403
Wednesday
February 22, 2006
3:30 - 4:20 pm