gps_learning

Using GPS to learn significant locations and predict movement across multiple users | PDF

What if your phone could automatically learn your daily schedule? Based on your patterns of movement, this system predicted where you might go next.

Using GPS to learn significant locations and predict movement across multiple users. Daniel Ashbrook and Thad Starner. Personal and Ubiquitous Computing, 7(5):275–286, October 2003.

Abstract: 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.

Other publications

Location modeling: From raw data to user models. Daniel Ashbrook and Thad Starner. In Proceedings of Workshop on Forecasting Presence and Availability at SIGCHI conference on Human Factors in Computing Systems (CHI) 2004.

Learning significant locations and predicting user movement with GPS. Daniel Ashbrook and Thad Starner. In Proceedings of the IEEE International Symposium on Wearable Computers (ISWC) 2002.

Enabling ad-hoc collaboration through schedule learning and prediction. Daniel Ashbrook and Thad Starner. In Proceedings of Workshop on Mobile Ad-Hoc Collaboration at SIGCHI conference on Human Factors in Computing Systems (CHI) 2002.