Recent advances in supervised deep learning techniques have demonstrated the
possibility to remotely measure human physiological vital signs (e.g.,
photoplethysmograph, heart rate) just from facial videos. However, the
performance of these methods heavily relies on the availability and diversity
of real labeled data. Yet, collecting large-scale real-world data with
high-quality labels is typically challenging and resource intensive, which also
raises privacy concerns when storing personal bio-metric data. Synthetic
video-based datasets (e.g., SCAMPS~\cite{mcduff2022scamps}) with
photo-realistic synthesized avatars are introduced to alleviate the issues
while providing high-quality synthetic data. However, there exists a
significant gap between synthetic and real-world data, which hinders the
generalization of neural models trained on these synthetic datasets. In this
paper, we proposed several measures to add real-world noise to synthetic
physiological signals and corresponding facial videos. We experimented with
individual and combined augmentation methods and evaluated our framework on
three public real-world datasets. Our results show that we were able to reduce
the average MAE from 6.9 to 2.0.

本文提出了在合成生理信号和相应的面部视频中添加真实世界噪声的几种方法，并在三个公开的真实世界数据集上评估了我们的框架，结果显示我们能够将平均 MAE 从 6.9 降低到 2.0。

使用合成的基于视频的数据训练鲁棒的深度生理测量模型

Training Robust Deep Physiological Measurement Models with Synthetic  Video-based Data

The use of cameras and computational algorithms for noninvasive, low-cost and
scalable measurement of physiological (e.g., cardiac and pulmonary) vital signs
is very attractive. However, diverse data representing a range of environments,
body motions, illumination conditions and physiological states is laborious,
time consuming and expensive to obtain. Synthetic data have proven a valuable
tool in several areas of machine learning, yet are not widely available for
camera measurement of physiological states. Synthetic data offer "perfect"
labels (e.g., without noise and with precise synchronization), labels that may
not be possible to obtain otherwise (e.g., precise pixel level segmentation
maps) and provide a high degree of control over variation and diversity in the
dataset. We present SCAMPS, a dataset of synthetics containing 2,800 videos
(1.68M frames) with aligned cardiac and respiratory signals and facial action
intensities. The RGB frames are provided alongside segmentation maps. We
provide precise descriptive statistics about the underlying waveforms,
including inter-beat interval, heart rate variability, and pulse arrival time.
Finally, we present baseline results training on these synthetic data and
testing on real-world datasets to illustrate generalizability.

使用计算算法和相机进行非侵入性、低成本和可扩展的生理（例如心肺）重要指标的测量非常吸引人。该研究引入了 Synthetic 数据集 SCAMPS 为相机测量生理指标提供完美标签，并以真实数据集作为测试集以说明可推广性。

SCAMPS: 监测生理信号的相机合成技术

SCAMPS: Synthetics for Camera Measurement of Physiological Signals

The need for remote tools for healthcare monitoring has never been more
apparent. Camera measurement of vital signs leverages imaging devices to
compute physiological changes by analyzing images of the human body. Building
on advances in optics, machine learning, computer vision and medicine these
techniques have progressed significantly since the invention of digital
cameras. This paper presents a comprehensive survey of camera measurement of
physiological vital signs, describing they vital signs that can be measured and
the computational techniques for doing so. I cover both clinical and
non-clinical applications and the challenges that need to be overcome for these
applications to advance from proofs-of-concept. Finally, I describe the current
resources (datasets and code) available to the research community and provide a
comprehensive webpage (this https URL) with links to these
resource and a categorized list of all the papers referenced in this article.

摄像头技术可以通过分析人体图像以计算生理变化，因此在遥测医疗监测方面有着极大的应用潜力，同时也存在一定的挑战和需要解决的问题。该研究论文综述了这一技术的现状、能够测量的生理指标及其计算方法，并涵盖了临床和非临床应用。