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

Nicolas Verstaevel (1) , Jean-Pierre Georgé (2) , Carole Bernon (2, 3) , Marie-Pierre Gleizes (2)

1 University of Wollongong [Australia]
2 IRIT-SMAC - Systèmes Multi-Agents Coopératifs
3 UT3 - Université Toulouse III - Paul Sabatier

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.

Mots clés: Smart health; Anomaly detection; Multi-agent systems

Domaines: Système multi-agents [cs.MA]; Intelligence artificielle [cs.AI]; Modélisation et simulation

Nicolas Verstaevel, Jean-Pierre Georgé, Carole Bernon, Marie-Pierre Gleizes. 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), Sep 2018, Trento, Italy. pp.70-79, ⟨10.1109/SASO.2018.00018⟩. ⟨hal-02191803⟩