rPPG-Toolbox: Deep Remote PPG Toolbox

TL;DR

The paper introduces rPPG-Toolbox, a comprehensive software toolkit that facilitates the development, benchmarking, and evaluation of remote photoplethysmography models using deep learning techniques, supporting reproducibility and progress in the field.

Contribution

It provides an open-source, systematic toolbox with multiple models, datasets, and evaluation tools for remote PPG measurement, addressing reproducibility and benchmarking challenges.

Findings

Supports multiple deep learning models for rPPG

Includes public benchmark datasets and data augmentation

Enables systematic evaluation and comparison

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, 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}