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, using context to determine how to act. Location is the most common form of context used by these agents to determine the user´s task. However, another potential use of location context 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.