Recognizing Activities and
Spatial Context Using Wearable Sensors
Proc. of Conference on Uncertainty in AI (UAI), 2006
Abstract
We introduce a new dynamic model with the capability of recognizing
both activities that an individual is performing as well as where
that individual is located. Our approach is novel in that it
utilizes a dynamic graphical model to jointly estimate both activity
and spatial context over time based on the simultaneous use of
asynchronous observations consisting of GPS measurements, and a
small mountable sensor board. Joint inference is quite desirable as
it has the ability to improve accuracy of the model and consistency
of the location and activity estimates. The parameters of our model
are trained on partially labeled data. We apply virtual evidence to
improve data annotation, giving the user high flexibility when
labeling training data. We present results indicating the
performance gains achieved by virtual evidence for data annotation
and the joint inference performed by our system.