Estimating Heart Rate and Rhythm via 3D Motion Tracking in Depth Video
Estimating Heart Rate and Rhythm via 3D Motion Tracking in Depth Video
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DOI：
10.1109/tmm.2017.2672198
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发表时间：
2017-07
影响因子：
7.3
通讯作者：
Cheng Yang;Gene Cheung;V. Stanković
中科院分区：
文献类型：
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作者：
Cheng Yang;Gene Cheung;V. Stanković
低成本的深度传感器，如微软Kinect，具有非接触式健康监测的潜力，对环境照明条件具有鲁棒性。然而，捕获的深度图像通常遭受高采集噪声，因此，处理它们以估计生物特征是困难的。在本文中，我们建议使用Kinect 2.0来捕获人类主体的深度视频，以估计他/她的心率和节奏;由于血液从心脏泵送通过头部循环，由于牛顿力学的微小振荡头部运动可以检测到周期性分析。具体来说，我们首先通过联合比特深度增强/去噪过程恢复捕获的深度视频，使用图形信号平滑度进行正则化。其次，我们跟踪整个深度视频自动检测到的头部区域，以推断3D运动矢量。检测到的矢量在循环中被反馈到深度恢复模块，以确保两个模块中的运动信息是一致的，从而提高恢复和运动跟踪的性能。第三，将计算的3D运动矢量投影到其主分量上用于1D信号分析，包括趋势去除、带通滤波和基于小波的运动去噪。最后，通过Welch功率谱分析来估计心率，并且通过峰值检测来计算心律。实验结果表明，准确估计的心率和节奏，使用我们提出的算法相比，率和节奏估计的便携式血氧计。
Low-cost depth sensors, such as Microsoft Kinect, have potential for noncontact health monitoring that is robust to ambient lighting conditions. However, captured depth images typically suffer from high acquisition noise, and hence, processing them to estimate biometrics is difficult. In this paper, we propose to capture depth video of a human subject using Kinect 2.0 to estimate his/her heart rate and rhythm; as blood is pumped from the heart to circulate through the head, tiny oscillatory head motion due to Newtonian mechanics can be detected for periodicity analysis. Specifically, we first restore a captured depth video via a joint bit-depth enhancement/denoising procedure, using a graph-signal smoothness prior for regularization. Second, we track an automatically detected head region throughout the depth video to deduce 3D motion vectors. The detected vectors are fed back to the depth restoration module in a loop to ensure that the motion information in two modules is consistent, improving performance of both restoration and motion tracking. Third, the computed 3D motion vectors are projected onto its principal component for 1D signal analysis, composed of trend removal, bandpass filtering, and wavelet-based motion denoising. Finally, the heart rate is estimated via Welch power spectrum analysis, and the heart rhythm is computed via peak detection. Experimental results show accurate estimation of the heart rate and rhythm using our proposed algorithm as compared to rate and rhythm estimated by a portable oximeter.