Sleep monitoring via depth video recording and analysis

Cheng Yang, G. Cheung, K. Chan, V. Stankovic

Research output: Contribution to journalConference Contributionpeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Number of pages6
JournalIEEE International Conference on Multimedia and Expo Workshops (ICMEW)
Publication statusPublished - 18 Jul 2014
Event 5th IEEE International Workshop on Hot Topics in 3D (Hot3D) - Chengdu, China
Duration: 18 Jul 2014 → …


  • bioelectric potentials
  • biomedical optical imaging
  • error statistics
  • medical image processing
  • image coding
  • neurophysiology
  • support vector machines


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