Remote camera measurement of the blood volume pulse via photoplethysmography
(rPPG) is a compelling technology for scalable, low-cost, and accessible
assessment of cardiovascular information. Neural networks currently provide the
state-of-the-art for this task and supervised training or fine-tuning is an
important step in creating these models. However, most current models are
trained on facial videos using contact PPG measurements from the fingertip as
targets/ labels. One of the reasons for this is that few public datasets to
date have incorporated contact PPG measurements from the face. Yet there is
copious evidence that the PPG signals at different sites on the body have very
different morphological features. Is training a facial video rPPG model using
contact measurements from another site on the body suboptimal? Using a recently
released unique dataset with synchronized contact PPG and video measurements
from both the hand and face, we can provide precise and quantitative answers to
this question. We obtain up to 40 % lower mean squared errors between the
waveforms of the predicted and the ground truth PPG signals using
state-of-the-art neural models when using PPG signals from the forehead
compared to using PPG signals from the fingertip. We also show qualitatively
that the neural models learn to predict the morphology of the ground truth PPG
signal better when trained on the forehead PPG signals. However, while models
trained from the forehead PPG produce a more faithful waveform, models trained
from a finger PPG do still learn the dominant frequency (i.e., the heart rate)
well.

远程摄像测量血容量脉搏通过光容积脉搏图（rPPG）是一种可伸缩、低成本、易于获取心血管信息的引人注目的技术。我们使用最新发布的同步接触式 PPG 和面部视频测量的独特数据集，对训练面部视频 rPPG 模型时是否使用来自身体其他部位的接触式测量数据进行优化进行了精确和定量的研究，结果显示，使用额头 PPG 信号相较于手指 PPG 信号，可以获得高达 40% 的均方误差降低，模型更好地学习了地面真实 PPG 信号的形态。然而，从手指 PPG 训练的模型仍然能很好地学习到主导频率（即心率）的特征。