Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

  • George Okeyo
  • Liming Chen
  • Hui Wang
  • Roy Sterritt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6406)

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • George Okeyo
    • 1
  • Liming Chen
    • 1
  • Hui Wang
    • 1
  • Roy Sterritt
    • 1
  1. 1.Computer Science Research Institute, School of Computing and MathematicsUniversity of UlsterNewtownabbeyUnited Kingdom

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