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Ontology-based Learning Framework for Activity Assistance in an Adaptive Smart Home

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

Okeyo, George, Chen, Liming, Wang, H and Sterritt, Roy (2011) Ontology-based Learning Framework for Activity Assistance in an Adaptive Smart Home. In: Activity Recognition in Pervasive Intelligent Environments, Atlantis Ambient and Pervasive Intelligence. (Eds: Chen, Liming, Nugent, CD, Biswas, Jit and Hoey, Jesse), Atlantis Press, pp. 237-262. ISBN 9789078677352 [Book section]

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Abstract

Activity and behaviour modelling are significant for activity recognition and personalizedassistance, respectively, in smart home based assisted living. Ontology-based activity andbehaviour modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) and behaviour models with rich semantics. However, theysuffer from incompleteness, inflexibility, and lack of adaptation. In this article, we proposea novel approach for learning and evolving activity and behaviour models. The approachuses predefined “seed” ADL ontologies to identify activities from sensor activationstreams. Similarly, we provide predefined, but initially unpopulated behaviour ontologiesto aid behaviour recognition. First, we develop algorithms that analyze logs of activitydata to discover new activities as well as the conditions for evolving the seed ADL ontologies. Consequently, we provide an algorithm for learning and evolving behaviours (or life habits) from these logs. We illustrate our approach through scenarios. The first scenarioshows how ADL models can be evolved to accommodate new ADL activities and peculiarities of individual smart home’s inhabitants. The second scenario describes how, subsequent to ADL learning and evolution, behaviours can be learned and evolved.

Item Type:Book section
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:17256
Deposited By:Dr Liming Chen
Deposited On:13 Apr 2011 11:41
Last Modified:13 Apr 2011 11:41

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