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Sleep monitoring via depth video recording and analysis

机译:通过深度视频记录和分析进行睡眠监控

摘要

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.
机译:睡眠质量极大地影响一个人的生理健康。传统的睡眠监测系统成本昂贵且具有侵入性,以至于干扰了临床患者的自然睡眠。在本文中,我们提出了一种仅使用记录的深度视频的廉价非侵入式睡眠监控系统。特别是,我们提出了由深度视频压缩和分析组成的两部分解决方案。对于采集和压缩,我们首先提出一种交替帧视频记录方案,以便在不同的时刻提取MS Kinect捕获的深度图像中11位中的8位,以使用H.264视频编解码器进行有效编码。在解码器上,可以通过基于块的搜索过程准确地恢复每个帧中未编码的3位。为了进行分析,我们估计每个深度图像中我们提出的双椭圆模型的参数。然后通过支持向量机检测睡眠事件,该向量机在一段时间内对估计的椭圆模型参数进行统计训练。实验结果首先表明,我们的深度视频压缩方案优于竞争方案,该方案仅记录了中高比特率区域中PSNR中的八个最高有效位。此外,我们还表明,使用我们训练有素的SVM,我们的监测可以检测出严重的睡眠事件,例如呼吸不足,并且成功率很高。

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