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)


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.


Lifestyle reassurance probabilistic models GMM telecare 


  1. 1.
    Barger, T.S., Brown, D.E., Alwan, M.: Health-status monitoring through analysis of behavioral patterns. IEEE Trans. on Sys., Man and Cybernetics 35, 22–27 (2005)CrossRefGoogle Scholar
  2. 2.
    Barnes, N.M., Edwards, H.M., Rose, D.A.D., Garner, P.: Lifestyle monitoring-technology for supported independence. Comp. & Control Eng. J. 9, 169–174 (1998)CrossRefGoogle Scholar
  3. 3.
    Brownsell, S., Blackburn, S., Hawley, M.: An evaluation of second and third generation telecare services in older people’s housing. J. of Telemedicine and Telecare 14, 8–12 (2008)CrossRefGoogle Scholar
  4. 4.
    Chan, M.T., Hoogs, A., Schmiederer, J., Petersen, M.: Detecting rare events in video using semantic primitives with HMM. In: 17th International Conference on Pattern Recognition, vol. 4, pp. 150–154 (2004)Google Scholar
  5. 5.
    Duda, R., Hart, P., Stork, G.: Pattern Classification. Wiley, Chichester (2001)zbMATHGoogle Scholar
  6. 6.
    Fawcett, T., Provost, F.: Activity Monitoring: Noticing interesting changes in behavior. In: The conference on Knowledge Discovery in Data, pp. 53–62 (1999)Google Scholar
  7. 7.
    Tarassenko, L., Nairac, A., Townsend, N., Buxton, I., Cowley, P.: Novelty detection for the identification of abnormalities. International Journal of Systems Science 31, 1427–1439 (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ohta, S., Nakamoto, H., Shinagawa, Y., Kishimoto, T.: Home Telehealth: Connecting Care Within the Community. Medical telematics, 198–209 (2006)Google Scholar
  9. 9.
    Osmani, V., Balasubramaniam, S., Botvich, D.: Human activity recognition in pervasive health-care: Supporting efficient remote collaboration. J. of Network and Computer Applications (in press)Google Scholar
  10. 10.
    Virone, G., Noury, N., Demongeot, J.: A system for automatic measurement of circadian activity deviations in telemedicine. IEEE Trans. on Biomedical Engineering 49, 1463–1469 (2002)CrossRefGoogle Scholar
  11. 11.
    Virone, G., Alwan, M., Dalal, S., Kell, S., Turner, B., Stankovic, J.A., Felder, R.: Behavioral Patterns of Older Adults in Assisted Living. IEEE Trans. on Information Technology in Biomedicine (in press)Google Scholar
  12. 12.
    Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised Activity Recognition Using Automatically Mined Common Sense. In: Proceedings of AAAI 2005, pp. 21–27 (2005)Google Scholar
  13. 13.
    Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-Supervised Adapted HMMs for Unusual Event Detection. IEEE Computer Vision and Pattern Recognition 1, 611–618 (2005)Google Scholar

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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