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The reliability and accuracy of estimating heart-rates from RGB video recorded on a consumer grade camera

机译:在消费者级相机上记录RGB视频的估算心率的可靠性和准确性

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Video Photoplethysmography (VPPG) is a numerical technique to process standard RGB video data of exposed human skin and extracting the heart-rate (HR) from the skin areas. Being a non-contact technique, VPPG has the potential to provide estimates of subject's heart-rate, respiratory rate, and even the heart rate variability of human subjects with potential applications ranging from infant monitors, remote healthcare and psychological experiments, particularly given the non-contact and sensor-free nature of the technique. Though several previous studies have reported successful correlations in HR obtained using VPPG algorithms to HR measured using the gold-standard electrocardiograph, others have reported that these correlations are dependent on controlling for duration of the video-data analyzed, subject motion, and ambient lighting. Here, we investigate the ability of two commonly used VPPG-algorithms in extraction of human heart-rates under three different laboratory conditions. We compare the VPPG HR values extracted across these three sets of experiments to the gold-standard values acquired by using an electrocardiogram or a commercially available pulse-oximeter. The two VPPG-algorithms were applied with and without KLT-facial feature tracking and detection algorithms from the Computer Vision MATLAB? toolbox. Results indicate that VPPG based numerical approaches have the ability to provide robust estimates of subject HR values and are relatively insensitive to the devices used to record the video data. However, they are highly sensitive to conditions of video acquisition including subject motion, the location, size and averaging techniques applied to regions-of-interest as well as to the number of video frames used for data processing.
机译:视频光电电机描绘(VPPG)是处理暴露人体皮肤的标准RGB视频数据并从皮肤区域提取心率(HR)的标准RGB视频数据的数值技术。作为一种非接触式技术,VPPG必须提供主体的心脏速率,呼吸速率,甚至具有潜在的应用范围从婴儿监控器,远程医疗和心理实验的人类受试者的心脏心率变异性估计的潜力,特别是考虑到非 - 联系和无传感器的技术性质。虽然以前的几项研究报告了使用VPPG算法获得的HR中的成功相关性,但是使用使用金标准的心电图测量的HR获得的HR,其他结果报告说这些相关性取决于分析的视频数据的持续时间,对象运动和环境照明的控制。在这里,我们研究了两个常用的VPPG算法在三种不同实验室条件下提取人心率率的能力。我们将通过使用心电图或市售的脉冲血氧计获得的金标准值进行比较跨这三组实验的VPPG HR值。从计算机Vision Matlab应用了两个VPPG算法,没有KLT-Facial特征跟踪和检测算法?工具箱。结果表明基于VPPG的数值方法具有提供对象HR值的稳定估计的能力,并且对用于记录视频数据的设备相对不敏感。然而,它们对视频采集条件非常敏感,包括对象运动,位置,大小和平均技术应用于兴趣区的区域以及用于数据处理的视频帧的数量。

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