Heart rate is an important physiological indicator of human health status. Existing remote heart rate measurement methods typically involve facial detection followed by signal extraction from the region of interest (ROI). These SOTA methods have three serious problems: (a) inaccuracies even failures in detection caused by environmental influences or subject movement; (b) failures for special patients such as infants and burn victims; (c) privacy leakage issues resulting from collecting face video. To address these issues, we regard the remote heart rate measurement as the process of analyzing the spatiotemporal characteristics of the optical flow signal in the video. We apply chaos theory to computer vision tasks for the first time, thus designing a brain-inspired framework. Firstly, using an artificial primary visual cortex model to extract the skin in the videos, and then calculate heart rate by time-frequency analysis on all pixels. Our method achieves Robust Skin Tracking for Heart Rate measurement, called HR-RST. The experimental results show that HR-RST overcomes the difficulty of environmental influences and effectively tracks the subject movement. Moreover, the method could extend to other body parts. Consequently, the method can be applied to special patients and effectively protect individual privacy, offering an innovative solution.

翻译：心率是人体健康状况的重要生理指标。现有远程心率测量方法通常依赖面部检测，继而从感兴趣区域提取信号。这些最先进方法存在三大严重问题：(a) 环境干扰或受试者运动导致检测不准确甚至失败；(b) 对婴幼儿及烧伤患者等特殊群体失效；(c) 采集面部视频引发隐私泄露风险。为解决上述问题，我们将远程心率测量视为分析视频中光流信号时空特性的过程，首次将混沌理论应用于计算机视觉任务，并据此设计仿脑框架。首先，利用初级视皮层人工模型提取视频中的皮肤区域，随后对所有像素进行时频分析以计算心率。该方法实现面向心率测量的鲁棒皮肤追踪，命名为HR-RST。实验结果表明，HR-RST克服了环境干扰的难题，有效追踪受试者运动。此外，该方法可延伸至人体其他部位，从而适用于特殊患者群体并有效保护个体隐私，提供了一项创新解决方案。