Learning Significant Locations and Predicting User Movement with GPS
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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.