Abstract
Many potential pervasive computing applications could use predictions of when a person will be at a certain place. Using a survey and GPS data from 34 participants in 11 households, we develop and test algorithms for predicting when a person will be at home or away. We show that our participants’ self-reported home/away schedules are not very accurate, and we introduce a probabilistic home/away schedule computed from observed GPS data. The computation includes smoothing and a soft schedule template. We show how the probabilistic schedule outperforms both the self-reported schedule and an algorithm based on driving time. We also show how to combine our algorithm with the best part of the drive time algorithm for a slight boost in performance.
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Krumm, J., Brush, A.J.B. (2011). Learning Time-Based Presence Probabilities. In: Lyons, K., Hightower, J., Huang, E.M. (eds) Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science, vol 6696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21726-5_6
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DOI: https://doi.org/10.1007/978-3-642-21726-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21725-8
Online ISBN: 978-3-642-21726-5
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