rPPG (Remote photoplethysmography) is a technology that measures and analyzes BVP (Blood Volume Pulse) by using the light absorption characteristics of hemoglobin captured through a camera. Analyzing the measured BVP can derive various physiological signals such as heart rate, stress level, and blood pressure, which can be applied to various applications such as telemedicine, remote patient monitoring, and early prediction of cardiovascular disease. rPPG is rapidly evolving and attracting great attention from both academia and industry by providing great usability and convenience as it can measure biosignals using a camera-equipped device without medical or wearable devices. 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 and artifacts, which degrade accuracy performance. We argue that fair and evaluable benchmarking is urgently required to overcome these challenges and make meaningful progress from both academic and commercial perspectives. In most existing work, models are trained, tested, and validated only on limited datasets. Even worse, 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: https://github.com/remotebiosensing/rppg

翻译：rPPG（远程光电容积描记术）是一种利用摄像头捕捉的血红蛋白光吸收特性来测量与分析BVP（血容量脉搏）的技术。通过分析测量所得的BVP，可推导出心率、压力水平和血压等多种生理信号，这些信号可应用于远程医疗、患者远程监测及心血管疾病早期预测等多个领域。rPPG凭借其无需医疗或可穿戴设备、仅需摄像头设备即可测量生物信号的卓越便利性，正迅速发展并引起学术界与工业界的广泛关注。尽管该领域已取得大量努力与进展，但仍存在严峻挑战，包括肤色差异、摄像头特性、环境光照以及其他噪声与伪影来源等问题，这些因素会降低准确性。我们认为，为了克服这些挑战并从学术与商业角度取得有意义的进展，迫切需要建立公平且可评估的基准测试。在现有大多数研究中，模型仅基于有限数据集进行训练、测试和验证。更甚者，部分研究缺乏可用的代码或可重复性，导致难以公平评估和比较性能。因此，本研究的目的是提供一个基准测试框架，用于评估多种rPPG技术（既包括传统非深度神经网络方法，也包括深度神经网络方法），并在广泛的数据集上进行公平评估与比较。GitHub网址：https://github.com/remotebiosensing/rppg