rPPG-Toolbox: Deep Remote PPG Toolbox
Abstract
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, unsupervised and supervised rPPG models with support for public benchmark datasets, data augmentation and systematic evaluation: https://github.com/ubicomplab/rPPG-Toolbox.
Cite
Text
Liu et al. "rPPG-Toolbox: Deep Remote PPG Toolbox." Neural Information Processing Systems, 2023.Markdown
[Liu et al. "rPPG-Toolbox: Deep Remote PPG Toolbox." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/liu2023neurips-rppgtoolbox/)BibTeX
@inproceedings{liu2023neurips-rppgtoolbox,
title = {{rPPG-Toolbox: Deep Remote PPG Toolbox}},
author = {Liu, Xin and Narayanswamy, Girish and Paruchuri, Akshay and Zhang, Xiaoyu and Tang, Jiankai and Zhang, Yuzhe and Sengupta, Roni and Patel, Shwetak and Wang, Yuntao and McDuff, Daniel},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/liu2023neurips-rppgtoolbox/}
}