Camera-based physiological measurement is a fast growing field of computer vision. Remote photoplethysmography (rPPG) utilizes imaging devices (e.g., cameras) to measure the peripheral blood volume pulse (BVP) via photoplethysmography, and enables cardiac measurement via webcams and smartphones. However, the task is non-trivial with important pre-processing, modeling, and post-processing steps required to obtain state-of-the-art results. Replication of results and benchmarking of new models is critical for scientific progress; however, as with many other applications of deep learning, reliable codebases are not easy to find or use. We present a comprehensive toolbox, rPPG-Toolbox, that contains unsupervised and supervised rPPG models with support for public benchmark datasets, data augmentation, and systematic evaluation: \url{https://github.com/ubicomplab/rPPG-Toolbox}
@article{arxiv.2210.00716,
title = {rPPG-Toolbox: Deep Remote PPG Toolbox},
author = {Xin Liu and Girish Narayanswamy and Akshay Paruchuri and Xiaoyu Zhang and Jiankai Tang and Yuzhe Zhang and Soumyadip Sengupta and Shwetak Patel and Yuntao Wang and Daniel McDuff},
journal= {arXiv preprint arXiv:2210.00716},
year = {2023}
}