Learning Situation Models in a Smart Home

This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different lay...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 39; no. 1; pp. 56 - 63
Main Authors: Brdiczka, O., Crowley, J.L., Reignier, P.
Format: Journal Article
Language:English
Published: United States IEEE 01.02.2009
Subjects:
Algorithms
Artificial Intelligence
Behavior - physiology
Computer Simulation
Context awareness
Context modeling
Context-aware services
Cybernetics
Data mining
Environment
Evolution
Extraction
Feedback
Frames
Human behavior
Human computer interaction
human-centered computing
Humans
Layout
Learning
Learning systems
Machine learning
Markov Chains
Mathematical models
Pattern Recognition, Automated - methods
Representations
Role
situation modeling
situation split
Smart homes
Sociometric Techniques
Supervised learning
situation modeling
human-centered computing
situation split
machine learning
Context awareness
ISSN:1083-4419, 1941-0492, 1941-0492
Online Access:Get full text
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Summary:This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment 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 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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ISSN:1083-4419
1941-0492
1941-0492
DOI:10.1109/TSMCB.2008.923526