Advertisement

Learning Time-Based Presence Probabilities

  • John Krumm
  • A. J. Bernheim Brush
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6696)

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.

Keywords

Location prediction presence prediction away prediction energy efficiency human routines 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gupta, M., Intille, S.S., Larson, K.: Adding GPS-Control to Traditional Thermostats: An Exploration of Potential Energy Savings and Design Challenges. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds.) Pervasive 2009. LNCS, vol. 5538, pp. 95–114. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Pesaran, A., Vlahinos, A., Stuart, T.: Cooling and Preheating of Batteries in Hybrid Electric Vehicles. In: 6th ASME-JSME Thermal Engineering Joint Conference (2003)Google Scholar
  3. 3.
    Ashbrook, D., Starner, T.: Using GPS to Learn Significant Locations and Predict Movement across Multiple Users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  4. 4.
    Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low-Level Sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Krumm, J., Horvitz, E.: Predestination: Inferring Destinations from Partial Trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Mozer, M.C., Vidmar, L., Dodier, R.M.: The Neurothermostat: Predictive Optimal Control of Residential Heating Systems. Advances in Neural Information Processing Systems 9, 953–959 (1997)Google Scholar
  7. 7.
    Gao, G., Whitehouse, K.: The Self-Programming Thermostat: Optimizing Setback Schedules based on Home Occupancy Patterns. In: First ACM Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings, Berkeley, CA USA (2009)Google Scholar
  8. 8.
    Szalay, A., et al.: Indexing the Sphere with the Hierarchical Triangular Mesh, Microsoft Research, MSR-TR-2005-123 (2005)Google Scholar
  9. 9.
    Carroll, J.: Workers’ Average Commute Round-Trip Is 46 Minutes in a Typical Day (2007), (cited 2010) http://www.gallup.com/poll/28504/Workers-Average-Commute-RoundTrip-Minutes-Typical-Day.aspx
  10. 10.
    Davidoff, S., Zimmerman, J., Dey, A.K.: How Routine Learners can Support Family Coordination. In: 28th ACM Conference on Human Factors in Computing Systems (CHI 2010), Atlanta, Georgia, USA (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • John Krumm
    • 1
  • A. J. Bernheim Brush
    • 1
  1. 1.Microsoft ResearchRedmondUSA

Personalised recommendations