MMPD: Multi-Domain Mobile Video Physiology Dataset
This provides a more comprehensive benchmark for researchers in non-contact physiological monitoring, though it is incremental as it addresses gaps in existing datasets rather than proposing a new method.
The authors tackled the lack of diverse public datasets for remote photoplethysmography (rPPG) by introducing the Multi-domain Mobile Video Physiology Dataset (MMPD), which includes 11 hours of recordings from 33 subjects to improve representation across skin tone, motion, and lighting conditions.
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.