Irregular patterns in the circadian rhythm may cause health problems, such as psychological or neurological disorders. Consequently, 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. The inputs of the platform are sequences of human postures, extensively used for analysis of activities of daily living and, more in general, for human behaviour understanding. The postures are acquired by using both ambient and wearable sensors that are time-of-flight 3D vision sensor, ultra-wideband radar sensor and triaxial accelerometer. The suggested platform aims to provide an abstraction layer with respect to the underlying sensing technologies, exploiting the postural information in common to all involved sensors (ie, standing, bending, sitting, lying down). Furthermore, in order to fill the lack of datasets containing long-term postural sequences, which are required in circadian rhythm analysis, a simulator of activities of daily living/postures has been proposed. The capability of the platform in providing a sensing invariant interface (ie, abstracted from any specific sensing technology) was demonstrated by preliminary results, exhibiting high accuracy in circadian rhythm anomalies detection using the three aforementioned sensors.
1 Jan 2017
Volume: 9 Pages: 123
Human Monitoring, Smart Health and Assisted Living: Techniques and Technologies