  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.
