Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationBerlin, Heidelberg
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 → …

Fingerprint

Ontology
Seed
Chemical activation
Semantics
Sensors
Assisted living

Cite this

Okeyo, George ; Chen, Liming ; Wang, Hui ; Sterritt, Roy. / Ontology-Enabled Activity Learning and Model Evolution in Smart Homes. Unknown Host Publication. Vol. 6406 Berlin, Heidelberg, 2010. pp. 67-82
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title = "Ontology-Enabled Activity Learning and Model Evolution in Smart Homes",
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.",
author = "George Okeyo and Liming Chen and Hui Wang and Roy Sterritt",
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doi = "10.1007/978-3-642-16355-5_8",
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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, Berlin, Heidelberg, pp. 67-82, The 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010), 1/10/10. https://doi.org/10.1007/978-3-642-16355-5_8

Ontology-Enabled Activity Learning and Model Evolution in Smart Homes. / Okeyo, George; Chen, Liming; Wang, Hui; Sterritt, Roy.

Unknown Host Publication. Vol. 6406 Berlin, Heidelberg, 2010. p. 67-82.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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AU - Okeyo, George

AU - Chen, Liming

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AU - Sterritt, Roy

PY - 2010/10

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N2 - 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.

AB - 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.

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