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野外远程光电容积脉搏波描记术(rPPG):通过在线网络摄像头进行远程心率成像。

Remote photoplethysmography (rPPG) in the wild: Remote heart rate imaging via online webcams.

机构信息

Humane Technology Lab, Università Cattolica del Sacro Cuore, Largo Gemelli, 1, 20100, Milan, Italy.

Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Via Magnasco, 2, 20149, Milan, Italy.

出版信息

Behav Res Methods. 2024 Oct;56(7):6904-6914. doi: 10.3758/s13428-024-02398-0. Epub 2024 Apr 17.

Abstract

Remote photoplethysmography (rPPG) is a low-cost technique to measure physiological parameters such as heart rate by analyzing videos of a person. There has been growing attention to this technique due to the increased possibilities and demand for running psychological experiments on online platforms. Technological advancements in commercially available cameras and video processing algorithms have led to significant progress in this field. However, despite these advancements, past research indicates that suboptimal video recording conditions can severely compromise the accuracy of rPPG. In this study, we aimed to develop an open-source rPPG methodology and test its performance on videos collected via an online platform, without control of the hardware of the participants and the contextual variables, such as illumination, distance, and motion. Across two experiments, we compared the results of the rPPG extraction methodology to a validated dataset used for rPPG testing. Furthermore, we then collected 231 online video recordings and compared the results of the rPPG extraction to finger pulse oximeter data acquired with a validated mobile heart rate application. Results indicated that the rPPG algorithm was highly accurate, showing a significant degree of convergence with both datasets thus providing an improved tool for recording and analyzing heart rate in online experiments.

摘要

远程光电容积脉搏波描记术 (rPPG) 是一种通过分析人的视频来测量心率等生理参数的低成本技术。由于在在线平台上运行心理实验的可能性和需求增加,人们对这项技术的关注度越来越高。商用相机和视频处理算法的技术进步使得该领域取得了重大进展。然而,尽管有这些进步,但过去的研究表明,不理想的视频录制条件会严重影响 rPPG 的准确性。在这项研究中,我们旨在开发一种开源的 rPPG 方法,并在没有参与者硬件和上下文变量(如照明、距离和运动)控制的情况下,通过在线平台收集的视频来测试其性能。通过两个实验,我们将 rPPG 提取方法的结果与用于 rPPG 测试的经过验证的数据集进行了比较。此外,我们收集了 231 个在线视频记录,并将 rPPG 提取的结果与使用经过验证的移动心率应用程序获取的手指脉搏血氧仪数据进行了比较。结果表明,rPPG 算法非常准确,与两个数据集都具有高度的一致性,从而为在线实验中记录和分析心率提供了一个改进的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ae6/11362249/44850fab1dea/13428_2024_2398_Fig1_HTML.jpg

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