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

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Abstract

Wearable computers have the potential to act as intelligent agents in everyday life and to 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.

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Acknowledgements

Many thanks to Jan-Derk Bakker for writing a Monte Carlo simulator. Thanks to Graham Coleman for writing visualisation tools and to MapBlast (http://www.mapblast.com) for having freely available maps. Funding for this project has been provided in part by NSF career grant number 0093291.

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Authors and Affiliations

  1. College Of Computing, Georgia Institute of Technology, Atlanta, GA , 30332-0280 , USA

    Daniel Ashbrook & Thad Starner

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  1. Daniel Ashbrook
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  2. Thad Starner
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Correspondence to Daniel Ashbrook or Thad Starner.

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Ashbrook, D., Starner, T. Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquit Comput 7, 275–286 (2003). https://doi.org/10.1007/s00779-003-0240-0

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  • DOI: https://doi.org/10.1007/s00779-003-0240-0

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