Multi-Sensor Platform for Automatic Disorders Detection in Circadian Rhythm

Anomalies in the circadian rhythm may cause psychological or neurological disorders, mainly in elderly people. Early detection of anomalies in circadian rhythm could be useful for the prevention of such problems. This work describes a multi-sensor platform for anomalies detection in circadian rhythm. Three detectors with different sensing principles are considered: a Time-Of-Flight camera, a MEMS wearable wireless accelerometer and an Ultra-Wideband radar. The inputs of the platform are sequences of human postures, even simulated, extensively used both for analysis of Activities of Daily Living and human behavior understanding. A postures simulator, calibrated on real experiments performed by each detector involved in the platform, has been implemented in order to compensate the lack of wide datasets containing long-term data for the analyzed context. Finally, a reasoner layer infers knowledge by using a suitable activity recognition module; by means of an unsupervised clustering technique, the reasoner is able to detect specific circadian anomalies, providing a tool for clinical evaluations. Experimental evaluation shows the effectiveness of the implemented solution and the ability to detect circadian anomalies at varying sensing technology.