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Learning Situation Models in a Smart Home

Oliver Brdiczka 1 James L. Crowley 2, 3, 1, 4 Patrick Reignier 4, 1
3 PRIMA - Perception, recognition and integration for observation of activity
CNRS - Centre National de la Recherche Scientifique : UMR5217, INPG - Institut National Polytechnique de Grenoble , UJF - Université Joseph Fourier - Grenoble 1, Inria Grenoble - Rhône-Alpes
Abstract : This article addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart envi- ronment is represented by a situation model describing environment, users and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and the evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
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Submitted on : Monday, January 11, 2016 - 6:29:12 PM
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  • HAL Id : hal-01253466, version 1



Oliver Brdiczka, James L. Crowley, Patrick Reignier. Learning Situation Models in a Smart Home. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Institute of Electrical and Electronics Engineers, 2008, 39 (1), pp.56-63. ⟨hal-01253466⟩



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