Skip to main content
placeholder image

A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People

Conference Paper


Download full-text (Open Access)

Abstract


  • In a context of a rapidly growing population of elderly people, this paper introduces a novel method for behavioural anomaly detection relying on a self-organized learning process. This method first models the Circadian Activity Rhythm of a set of sensors and compares it to a nominal profile to determine variations in patients’ activities. The anomalies are detected by a multi-agent system as a linear relation of those variations, weighted by influence parameters. The problem of adaptation to a particular patient then becomes the problem of learning the adequate influence parameters. Those influence parameters are self-adjusted, using feedback provided at any time by the medical staff. This approach is evaluated on a synthetic environment and results show both the capacity to effectively learn influence parameters and the resilience of this system to parameter size. Details on the ongoing real-world experimentation are provided.

UOW Authors


  •   Verstaevel, Nicolas
  •   George, Jean-Pierre (external author)
  •   Bernon, Carole (external author)
  •   Gleizes, Marie-Pierre (external author)

Publication Date


  • 2018

Citation


  • Verstaevel, N., George, J., Bernon, C. & Gleizes, M. (2018). A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People. 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018 (pp. 70-79). United States: IEEE.

Scopus Eid


  • 2-s2.0-85061912299

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1295&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/268

Start Page


  • 70

End Page


  • 79

Place Of Publication


  • United States

Abstract


  • In a context of a rapidly growing population of elderly people, this paper introduces a novel method for behavioural anomaly detection relying on a self-organized learning process. This method first models the Circadian Activity Rhythm of a set of sensors and compares it to a nominal profile to determine variations in patients’ activities. The anomalies are detected by a multi-agent system as a linear relation of those variations, weighted by influence parameters. The problem of adaptation to a particular patient then becomes the problem of learning the adequate influence parameters. Those influence parameters are self-adjusted, using feedback provided at any time by the medical staff. This approach is evaluated on a synthetic environment and results show both the capacity to effectively learn influence parameters and the resilience of this system to parameter size. Details on the ongoing real-world experimentation are provided.

UOW Authors


  •   Verstaevel, Nicolas
  •   George, Jean-Pierre (external author)
  •   Bernon, Carole (external author)
  •   Gleizes, Marie-Pierre (external author)

Publication Date


  • 2018

Citation


  • Verstaevel, N., George, J., Bernon, C. & Gleizes, M. (2018). A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People. 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018 (pp. 70-79). United States: IEEE.

Scopus Eid


  • 2-s2.0-85061912299

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1295&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/268

Start Page


  • 70

End Page


  • 79

Place Of Publication


  • United States