Abstract
This paper deals with the smart house occupant prediction issue based on daily life activities. Based on data provided by non intrusive sensors and devices, our approach uses supervised learning technics to predict the house occupant. We applied Support Vector Machines (SVM) classifier to build a Behavior Classification Model (BCM) and learn the users’ habits when they perform activities for predicting and identifying the house occupant. To test the model, we have analyzed the early morning routine activity of six users at the DOMUS apartment and two users of the publicly available dataset of the Washington State University smart apartment tesbed. The results showed a high prediction precision and demonstrate that each user has his own manner to perform his morning activity, and can be easily identified by just learning his habits.
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References
Mit nhouse, http://architecture.mit.edu/housen/web.
A. Pentland, Smart rooms, Scientific American. 274(4), 68–76, (1996).
Georgia tech aware home research initiative, http://www.cc.gatech.edu/fce/ahri.
E. Vierck and K. Hodges, Aging: Lifestyles, Work and Money. (Greenwood Press, 2005).
R. J. Orr and G. D. Abowd. The smart floor: a mechanism for natural user identification and tracking. In Conference on Human Factors in Computing Systems, pp. 275–276, The Hague,The Netherlands, (2000). ACM.
M. Addlesee, A. H. Jones, F. Livesey, and F. S. Samaria, The orl active floor, IEEE Personal Communications. 4, 35–41, (1997).
W.-T. W. J.-H. R. J.-S. Yun, S.-H. Lee. The user identification system using walking pattern over the ubifloor. pp. 1046–1050, Gyeongju, Korea, (2003).
J. Little and J. E. Boyd, Recognizing people by their gait: The shape of motion, Videre. 1, 1–32, (1998).
K. R. Cuntoor, A. Kale, A. N. Rajagopalan, N. Cuntoor, and V. Krüger. Gait-based recognition of humans using continuous hmms. In in Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 321–326, (2002).
A. Jain, L. Hong, and S. Pankanti, Biometric identification, Commun. ACM. 43(2), 90–98, (2000). ISSN 0001-0782. doi: http://doi.acm.org/10.1145/328236.328110.
A. K. Jain, A. Ross, and S. Prabhakar, An introduction to biometric recognition, IEEE Trans. on Circuits and Systems for Video Technology. 14(1), 4–20, (2004).
Domus, http://domus.usherbrooke.ca/.
P. Rashidi and D. Cook, Keeping the resident in the loop: Adapting the smart home to the user, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on. 39(5), 949–959 (Sept., 2009). ISSN 1083-4427. doi: 10.1109/TSMCA.2009.2025137.
V. Vapnik, Statistical LearningTheory. (Wiley-Interscience, September 1998). ISBN 0471030031.
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. (Cambridge University Press, New York, NY, USA, 2004). ISBN 0521813972.
R. Debnath and H. Takahashi, Kernel selection for the support vector machine(biocybernetics, neurocomputing), IEICE transactions on information and systems. 87(12), 2903–2904, (2004).
R. J. A. Little and D. B. Rubin, Statistical analysis with missing data. (John Wiley & Sons, Inc., New York, NY, USA, 2002). ISBN 978-0471183860.
D. Cook and M. Schmitter-Edgecombe, Assessing the quality of activities in a smart environment, Methods of Information in Medicine. 48(5), (2009).
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. (Morgan Kaufmann, 2005), 2 edition.
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection. 2(12), 1137–1143, (1995).
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© 2011 Atlantis Press
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Kadouche, R., Pigot, H., Abdulrazak, B., Giroux, S. (2011). User’s Behavior Classification Model for Smart Houses Occupant Prediction. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol 4. Atlantis Press. https://doi.org/10.2991/978-94-91216-05-3_7
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DOI: https://doi.org/10.2991/978-94-91216-05-3_7
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