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。

用不同身体部位的视频和目标进行训练的 rPPG 模型有多次优？

How Suboptimal is Training rPPG Models with Videos and Targets from  Different Body Sites?

Remote Photoplethysmography (rPPG) is a technology that utilizes the light
absorption properties of hemoglobin, captured via camera, to analyze and
measure blood volume pulse (BVP). By analyzing the measured BVP, various
physiological signals such as heart rate, stress levels, and blood pressure can
be derived, enabling applications such as the early prediction of
cardiovascular diseases. rPPG is a rapidly evolving field as it allows the
measurement of vital signals using camera-equipped devices without the need for
additional devices such as blood pressure monitors or pulse oximeters, and
without the assistance of medical experts. Despite extensive efforts and
advances in this field, serious challenges remain, including issues related to
skin color, camera characteristics, ambient lighting, and other sources of
noise, which degrade performance accuracy. We argue that fair and evaluable
benchmarking is urgently required to overcome these challenges and make any
meaningful progress from both academic and commercial perspectives. In most
existing work, models are trained, tested, and validated only on limited
datasets. Worse still, some studies lack available code or reproducibility,
making it difficult to fairly evaluate and compare performance. Therefore, the
purpose of this study is to provide a benchmarking framework to evaluate
various rPPG techniques across a wide range of datasets for fair evaluation and
comparison, including both conventional non-deep neural network (non-DNN) and
deep neural network (DNN) methods. GitHub URL:
this https URL

远程光电容积法（rPPG）是一种利用通过摄像头捕捉的血红蛋白的光吸收特性来分析和测量血容量脉搏（BVP）的技术。通过分析测量得到的 BVP，可以推导出各种生理信号，如心率、应激水平和血压，从而实现心血管疾病的早期预测。然而，存在着与皮肤颜色、摄像头特性、环境光照和其他噪声源相关的严重挑战，这些问题降低了性能准确性。本研究目的在于提供一个基准测试框架，以公平评估和比较各种 rPPG 技术，包括传统的非深度神经网络（non-DNN）和深度神经网络（DNN）方法。

远程生物感知：用于公平评估 rPPG 的开源基准框架

Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation  of rPPG

Remote photoplethysmography (rPPG) technology has drawn increasing attention
in recent years. It can extract Blood Volume Pulse (BVP) from facial videos,
making many applications like health monitoring and emotional analysis more
accessible. However, as the BVP signal is easily affected by environmental
changes, existing methods struggle to generalize well for unseen domains. In
this paper, we systematically address the domain shift problem in the rPPG
measurement task. We show that most domain generalization methods do not work
well in this problem, as domain labels are ambiguous in complicated
environmental changes. In light of this, we propose a domain-label-free
approach called NEuron STructure modeling (NEST). NEST improves the
generalization capacity by maximizing the coverage of feature space during
training, which reduces the chance for under-optimized feature activation
during inference. Besides, NEST can also enrich and enhance domain invariant
features across multi-domain. We create and benchmark a large-scale domain
generalization protocol for the rPPG measurement task. Extensive experiments
show that our approach outperforms the state-of-the-art methods on both
cross-dataset and intra-dataset settings.

本文介绍了一种名为 NEST 的基于神经元结构建模的无领域标签方法，用于解决远程光电容积脉搏 (rPPG) 测量任务中的领域转移问题，并在多个数据集的测试中表现出优异的通用性能。