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Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance

  • Fabien Cardinaux
  • Simon Brownsell
  • Mark Hawley
  • David Bradley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

As a consequence of the growing number of older and vulnerable people, health and care providers are increasingly considering new approaches to support people in their own homes. In this context, lifestyle reassurance analyses data collected from a range of sensors to determine a person’s ‘routine’ and highlights any important changes. This paper proposes a new approach for detection of individual deviation from normal behaviour focusing on building probabilistic models of behaviour based on a set of activity attributes. Models are trained using only normal behaviour. Variations from the models are considered as abnormal behaviours and these can be highlighted for subsequent review or intervention. Case study experiments with real life data suggest that some users’ activities follow regular patterns and that these patterns can be learned with probabilistic models.

Keywords

Lifestyle reassurance probabilistic models GMM telecare 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fabien Cardinaux
    • 1
  • Simon Brownsell
    • 1
  • Mark Hawley
    • 1
  • David Bradley
    • 2
  1. 1.School of Health and Related ResearchUniversity of SheffieldUK
  2. 2.School of Science & EngineeringUniversity of Abertay DundeeUK

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