MMPD: Multi-Domain Mobile Video Physiology Dataset

TL;DR

The paper introduces MMPD, a comprehensive mobile phone video dataset for remote photoplethysmography, addressing gaps in diversity and size of existing datasets to improve physiological signal measurement accuracy.

Contribution

It provides a large, diverse, multi-domain dataset recorded with mobile phones, including extensive labels, to advance research in mobile rPPG technology.

Findings

Dataset verified with mainstream methods

Includes diverse skin tones, motions, and lighting conditions

Supports improved accuracy in physiological signal measurement

Abstract

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.