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Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Okeyo, George, Chen, Liming, Wang, Hui and Sterritt, Roy (2010) Ontology-Enabled Activity Learning and Model Evolution in Smart Homes. In: The 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010), Xian China. Springer-Verlag. Vol 6406 6 pp. [Conference contribution]

Full text not available from this repository.

DOI: 10.1007/978-3-642-16355-5_8


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.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:17258
Deposited By:Dr Liming Chen
Deposited On:07 Apr 2011 15:25
Last Modified:07 Apr 2011 15:25

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