ImViA-EA7535, Univ. Bourgogne Franche-Comté, 21000 Dijon, France.
LPL, CNRS-UMR7538, Univ. Paris 13, 93430 Villetaneuse, France.
Sensors (Basel). 2020 May 12;20(10):2752. doi: 10.3390/s20102752.
Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.
许多先前的研究表明，远程光体积描记术 (rPPG) 可以非常准确地测量心率 (HR) 信号。脉搏率变异度 (PRV) 信号的远程测量也是可能的，但这要复杂得多，因为需要检测时间 rPPG 信号上的峰值，而该信号通常噪声较大，时间分辨率比接触式设备获得的 PPG 信号低。由于 PRV 信号对于各种应用（例如远程识别压力和情绪）至关重要，因此 rPPG 对 PRV 测量的改进是一项关键任务。已经研究了基于接触的 PRV 测量，但远程测量的 PRV 研究非常有限。在本文中，我们提出使用周期性方差最大化 (PVM) 方法来提取 rPPG 信号，并使用事件相关的双窗算法来提高 PRV 测量的峰值检测。我们做出了以下几点贡献。首先，我们表明新提出的 PVM 方法和双窗算法可用于非接触场景中的 PRV 测量。其次，我们提出了一种自适应确定双窗方法参数的方法。第三，我们将该算法与其他用于改善非接触 PRV 测量的方法（例如斜率和函数 (SSF) 方法和局部最大值方法）进行了比较。我们计算了几个特征，并根据接触式设备提供的基准对准确性进行了比较。我们的实验表明，该算法的性能优于所有其他算法。
