The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning Situation Models in a Smart Home
Open accessAbstract
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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Citations
Cites
Intelligibility and Accountability: Human Considerations in Context-Aware Systems
Bellotti, V., & Edwards, K. (2001) · Human–Computer Interaction · doi:10.1207/s15327051hci16234_05 · Cited by 471
Cited by
Context Aware Computing for The Internet of Things: A Survey
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.042313.00197 · Cited by 2,861
Sensor-Based Activity Recognition
Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074
A Survey on Ambient Intelligence in Healthcare
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2262913 · Cited by 630
Activity recognition on streaming sensor data
Krishnan, N. C., & Cook, D. J. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2012.07.003 · Cited by 488
A survey of data fusion in smart city applications
Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U.-X. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.05.004 · Cited by 320
Metadata
- Authors
- Brdiczka, O., Crowley, J. L., & Reignier, P.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Published
- 2009-02
- Volume
- 39
- Issue
- 1
- Pages
- 56-63
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2166472401
- Cited by
- 173
- References
- 1
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- green
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- Brdiczka, O., Crowley, J. L., & Reignier, P. (2009). Learning Situation Models in a Smart Home. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). https://doi.org/10.1109/tsmcb.2008.923526
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
