Activity and behaviour modelling are significant for activity recognition and personalized assistance, respectively, in smart home based assisted living. Ontology-based activity and behaviour modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) and behaviour models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this article, we propose a novel approach for learning and evolving activity and behaviour models. The approach uses predefined “seed” ADL ontologies to identify activities from sensor activation streams. Similarly, we provide predefined, but initially unpopulated behaviour ontologies to aid behaviour recognition. First, we develop algorithms that analyze logs of activity data 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 scenario shows 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.
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