Pervasive 2011: Pervasive Computing pp 79-96 | Cite as
Learning Time-Based Presence Probabilities
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 routinesPreview
Unable to display preview. Download preview PDF.
References
- 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.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.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.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.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.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.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.Szalay, A., et al.: Indexing the Sphere with the Hierarchical Triangular Mesh, Microsoft Research, MSR-TR-2005-123 (2005)Google Scholar
- 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.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