Abstract
In order to act intelligently, a smart environment needs to have a notion about its users. Hidden Markov models are especially suited to recognize for example the state of a meeting in a smart meeting room, as they can cope with the noisy and intermittent sensor values. However, modeling the user behavior as an HMM is challenging, because of the high degrees of freedom the users have when acting in such a smart environment. Therefore, we compare two methods that ease the automatic generation of HMM and express the human behavior.
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Garner, S.R.: Weka: The waikato environment for knowledge analysis. In: Proc. of the New Zealand Computer Science Research Students Conference, pp. 57–64 (1995)
Hartmann, B., Klemmer, S.R.: Reflective physical prototyping through integrated design, test, and analysis. In: UIST 2006, pp. 299–308. ACM Press, New York (2006)
Björn, H., Leith, A., Manas, M., Klemmer, S.R.: Authoring sensor-based interactions by demonstration with direct manipulation and pattern recognition. In: CHI 2007: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 145–154. ACM Press, New York (2007)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Reisse, C., Kirste, T.: A distributed mechanism for device cooperation in Smart Environments. In: Advances in Pervasive Computing. Adjunct proceedings of the 6th International Conference on Pervasive Computing, Sydney, Australia, pp. 53–56 (2008)
Giersich, M., Forbrig, P., Fuchs, G., Kirste, T., Reichart, D., Schumann, H.: Towards an integrated approach for task modeling and human behavior recognition. In: Jacko, J.A. (ed.) HCI 2007. LNCS, vol. 4550, pp. 1109–1118. Springer, Heidelberg (2007)
Paterno, F., Mancini, C., Meniconi, S.: Concurtasktrees: A diagrammatic notation for specifying task models. In: INTERACT 1997: Proceedings of the IFIP TC13 Interantional Conference on Human-Computer Interaction, London, UK, pp. 362–369. Chapman & Hall, Ltd., Boca Raton (1997)
Jeff, A.B.: What HMMs Can Do. UWEE Technical Report UWEETR-2002-0003, Department of Electrical Engineering, University of Washington (January 2002)
Burghardt, C., Giersich, M., Kirste, T.: Synthesizing probabilistic models for team activities using partial order planning. In: Ami Workshop, KI-Konferenz 2007 (2007)
Fikes, R., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3/4), 189–208 (1971)
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Burghardt, C., Kirste, T. (2009). A Probabilistic Approach for Modeling Human Behavior in Smart Environments. In: Duffy, V.G. (eds) Digital Human Modeling. ICDHM 2009. Lecture Notes in Computer Science, vol 5620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02809-0_22
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DOI: https://doi.org/10.1007/978-3-642-02809-0_22
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