Learning Significant Locations and Predicting User Movement with GPS
Wearable computers have the potential to act as intelligent agents in
everyday life and assist the user in a variety of tasks depending on
the context. Location is the most common form of context used by these
agents to determine the user's task. However, another potential use
is the creation of a predictive model of the user's future movements.
We present a system that automatically clusters GPS data taken over
an extended period of time into meaningful locations at multiple
scales. These locations are then incorporated into a Markov model that
can be consulted for use with a variety of applications in both
single-user and collaborative scenarios.
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