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

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.