Remote photoplethysmography (rPPG) is an attractive method for noninvasive, convenient and concomitant measurement of physiological vital signals. Public benchmark datasets have served a valuable role in the development of this technology and improvements in accuracy over recent years.However, there remain gaps in the public datasets.First, despite the ubiquity of cameras on mobile devices, there are few datasets recorded specifically with mobile phone cameras. Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e.g., skin tone), behaviors (e.g., motion) and environment (e.g., lighting conditions). In an effort to help the field advance, we present the Multi-domain Mobile Video Physiology Dataset (MMPD), comprising 11 hours of recordings from mobile phones of 33 subjects. The dataset is designed to capture videos with greater representation across skin tone, body motion, and lighting conditions. MMPD is comprehensive with eight descriptive labels and can be used in conjunction with the rPPG-toolbox. The reliability of the dataset is verified by mainstream unsupervised methods and neural methods. The GitHub repository of our dataset: https://github.com/THU-CS-PI/MMPD_rPPG_dataset.

翻译：远程光电容积图（rPPG）是一种非侵入性、方便和同时测量生理信号的有吸引力的方法。公共基准数据集在近年来有助于这项技术的发展和精度的提高。然而，公共数据集仍然存在一些空白。首先，尽管移动设备上普及了摄像头，但是专门使用移动电话摄像头记录的数据集很少。其次，大多数数据集规模相对较小，因此在外观（如肤色）、行为（如运动）和环境（如光照条件）上具有局限性。为了帮助该领域取得进展，我们提出了多领域移动视频生理数据集（MMPD），包括33个对象的移动电话录制的11小时视频。该数据集旨在捕获具有更好的肤色、身体运动和光照条件的视频。 MMPD数据集具有八个描述性标签，并可与rPPG-toolbox同时使用。使用主流的无监督方法和神经方法验证了数据集的可靠性。我们的数据集GitHub库：https://github.com/THU-CS-PI/MMPD_rPPG_dataset。