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：远程光电容积描记法（rPPG）是一种用于非侵入性、便捷且同步测量生理生命信号的有效方法。近年来，公开基准数据集在促进该技术发展及提升准确率方面发挥了重要作用。然而，现有公开数据集仍存在不足。首先，尽管移动设备摄像头已广泛普及，但专门使用手机摄像头拍摄的数据集仍较为稀缺。其次，多数数据集规模相对较小，导致其在外观（如肤色）、行为（如运动）及环境（如光照条件）方面的多样性受限。为助力该领域的发展，我们提出了多领域移动视频生理数据集（MMPD），该数据集包含33名受试者、总时长11小时的手机录制视频。本数据集旨在更全面地覆盖不同肤色、身体运动和光照条件下的视频采集。MMPD数据集拥有8种描述性标签，可与rPPG工具箱结合使用。我们通过主流无监督方法及神经网络方法验证了其可靠性。数据集GitHub仓库地址：https://github.com/THU-CS-PI/MMPD_rPPG_dataset。