Chapter

Ubiquitous Intelligence and Computing

Volume 6406 of the series Lecture Notes in Computer Science pp 67-82

Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

  • George OkeyoAffiliated withComputer Science Research Institute, School of Computing and Mathematics, University of Ulster
  • , Liming ChenAffiliated withComputer Science Research Institute, School of Computing and Mathematics, University of Ulster
  • , Hui WangAffiliated withComputer Science Research Institute, School of Computing and Mathematics, University of Ulster
  • , Roy SterrittAffiliated withComputer Science Research Institute, School of Computing and Mathematics, University of Ulster

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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.

Keywords

Activity modelling activity learning ontology evolution smart homes ambient assisted living