Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

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Abstract

Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home’s inhabitants.
Original languageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages67-82
Number of pages6
Volume6406
DOIs
Publication statusPublished - Oct 2010
EventThe 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010) - Xian China
Duration: 1 Oct 2010 → …

Conference

ConferenceThe 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010)
Period1/10/10 → …

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  • Cite this

    Okeyo, G., Chen, L., Wang, H., & Sterritt, R. (2010). Ontology-Enabled Activity Learning and Model Evolution in Smart Homes. In Unknown Host Publication (Vol. 6406, pp. 67-82). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-16355-5_8