Abstract
Pervasive sensors in the home have a variety of applications including energy minimization, activity monitoring for elders, and tutors for household tasks such as cooking. Many of the common sensors today are binary, e.g. IR motion sensors, door close sensors, and floor pressure pads. Predicting user behavior is one of the key enablers for applications. While we consider smart home data here, the general problem is one of predicting discrete human actions. Drawing on Activity Theory, the language as action principle, and speech understanding research, we argue that smoothed n-grams are very appropriate for this task. We built such a model and applied it to data gathered from 3 smart home installations. The data showed a classic Zipf or power-law distribution, similar to speech and language. We found that the predictive accuracy of the n-gram model ranges from 51% to 39%, which is significantly above the baseline for the deployments of 16, 76 and 70 sensors. While we cannot directly compare this result with other work (lack of shared data), by examination of high entropy zones in the datasets (e.g. the kitchen triangle) we argue that accuracies around 50% are best possible for this task.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beaudin, J., Intille, S., Tapia, E.: Lessons Learned Using Ubiquitous Sensors for Data Collection in Real Homes. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI 2004 (2004)
Brumitt, B., Meyers, B., Krumm, J., Kern, A., Shafer, S.: EasyLiving: Technologies for intelligent environments. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 12–29. Springer, Heidelberg (2000)
Burke, K.: Language as Symbolic Action. University of California Press (1966)
Intel Corporation: Digital Home, Technology and Research at Intel, http://www.cc.gatech.edu/fce/ecl/projects/dfp/index.html
Jelinek, F.: Statistical Methods for Speech Recognition, p. 58. MIT Press, Cambridge, Massachusetts (1997)
Katz, S.M.: Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recogniser. IEEE Transactions on Acoustic, Speech and Signal Processing 35(3), 400–401 (1987)
Liao, L., Fox, D., Kautz, H.: Learning and Inferring Transportation Routines. In: Proceedings of AAAI 2004 (2004)
Mozer, M.: Lessons from and Adaptive House. In: Cook, D., Das, R. (eds.) Smart Environments: Technologies, protocols, and applications, pp. 273–294. J. Wiley and Sons, Hoboken, NJ (2005)
Philipose, M., et al.: Inferring Activities from Interactions with Objects. In: Proceedings of the Conference on Pervasive Computing, October 2004, pp. 50–57 (2004)
Rao, S., Cook, D.J.: Identifying Tasks and Predicting Actions in Smart Homes using Unlabeled Data. In: Proceedings of the Machine Learning Workshop on The Continuum from Labeled to Unlabeled Data (2003)
Roukos, S.: Language Representation. In: Cole, R.A., et al. (eds.) Survey of the State of the Art in Human Language Technology, Center for Spoken Language Understanding CSLU, Carnegie Mellon University (1995)
Rowan, J.: Digital Family Portrait project, http://www.cc.gatech.edu/fce/ecl/projects/dfp/index.html
Rowan, J., Mynatt, E.D.: Digital family portraits: Providing peace of mind for extended family members. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2001), pp. 333–340. ACM Press, Seattle, Washington (2001)
Roy, A., et al.: Location Aware Resource Management in Smart Homes. In: Proceedings of the Conference on Pervasive Computing (2003)
Shannon, C.E.: A Mathematical Theory of Communication. The Bell System Technical Journal 27, 379–423 (1948)
Simon, H.A.: On a class of skew distribution functions. Biometrika 42, 425–440 (1955)
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
Vygotsky, L.S.: Mind in Society: The development of higher psychological processes. Cole, M., John-Steiner, V., Scribner, S., Souberman, E. (eds.) Harvard University Press (1978)
Wertsch, J.: Mind As Action. Oxford University Press, Oxford (1998)
Willis, J.C., Yule, G.U.: Some statistics of evolution and geographical distribution in plants and animals, and their significance. Nature 109, 177–179 (1922)
Wilson, D.H., Atkeson, C.G.: Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005)
Young, S., et al.: The HTK Book, Microsoft Corporation and Cambridge University, 3.2.1 edition (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aipperspach, R., Cohen, E., Canny, J. (2006). Modeling Human Behavior from Simple Sensors in the Home. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds) Pervasive Computing. Pervasive 2006. Lecture Notes in Computer Science, vol 3968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11748625_21
Download citation
DOI: https://doi.org/10.1007/11748625_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33894-9
Online ISBN: 978-3-540-33895-6
eBook Packages: Computer ScienceComputer Science (R0)