Abstract
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.
Abstract (translated)
的心率是评估人类健康状况的重要生理指标。现有的远程心率测量方法通常包括从感兴趣区域（ROI）的信号提取，然后进行面部检测。这些SOTA方法有三个严重问题：（一）由于环境因素或被检测者运动等原因导致的准确性甚至失败；（二）对特殊患者（如婴儿和烧伤患者）的失败；（三）通过收集面部视频导致的隐私泄露问题。为解决这些问题，我们将其视为分析视频中光学流信号的时空特征的过程。我们首先使用人工primary视觉皮层模型提取视频中的皮肤，然后通过时间-频率分析计算所有像素的心率。我们的方法实现了名为HR-RST的心率测量中的鲁棒皮肤跟踪。实验结果表明，HR-RST克服了环境因素带来的困难，有效跟踪了被检测者的运动。此外，该方法还可以应用于其他身体部位。因此，该方法可以应用于特殊患者，有效保护个人隐私，提供了一种创新的解决方案。
URL
https://arxiv.org/abs/2404.07687
