Sleep monitoring via depth video compression & analysis

Quality of sleep greatly affects a person's physiological well-being. Traditional sleep monitoring systems are expensive in cost and intrusive enough that they disturb natural sleep of clinical patients. In this paper, we propose an inexpensive non-intrusive sleep monitoring system using recorded depth video only. In particular, we propose a two-part solution composed of depth video compression and analysis. For acquisition and compression, we first propose an alternating-frame video recording scheme, so that different 8 of the 11 bits in MS Kinect captured depth images are extracted at different instants for efficient encoding using H.264 video codec. At decoder, the uncoded 3 bits in each frame can be recovered accurately via a block-based search procedure. For analysis, we estimate parameters of our proposed dual-ellipse model in each depth image. Sleep events are then detected via a support vector machine trained on statistics of estimated ellipse model parameters over time. Experimental results show first that our depth video compression scheme outperforms a competing scheme that records only the eight most significant bits in PSNR in mid- to high-bitrate regions. Further, we show also that our monitoring can detect critical sleep events such as hypopnoea using our trained SVM with very high success rate.