Towards an Intelligent Bed Sensor: Non-intrusive Monitoring of Sleep Irregularities with Computer Vision Techniques

This paper proposes a novel approach for monitoring sleep using pressure data. The goal of sleep monitoring is to detect and log events of normal breathing, sleep apnea and body motion. The proposed approach is based on translating the signal data to the image domain by computing a sequence of inter-frame similarity matrices from pressure maps acquired with a mattress of pressure sensors. Periodicity analysis was performed on similarity matrices via a new algorithm based on segmentation of elementary patterns using the watershed transform, followed by aggregation of quasi-rectangular patterns into breathing cycles. Once breathing events are detected, all remaining elementary patterns aligned on the main diagonal are considered as belonging to either apnea or motion events. The discrimination between these two events is based on detecting movement times from a statistical analysis of pressure data. Experimental results confirm the validity of our approach.