rPPG-Toolbox: Deep Remote PPG Toolbox
This work addresses the problem of unreliable codebases for researchers in computer vision and healthcare, enabling easier replication and benchmarking of rPPG models, though it is incremental as it builds on existing methods without introducing new paradigms.
The authors tackled the challenge of replicating and benchmarking remote photoplethysmography (rPPG) models for camera-based physiological measurement by developing rPPG-Toolbox, a comprehensive toolbox that includes unsupervised and supervised models, supports public datasets, data augmentation, and systematic evaluation, resulting in an open-source resource to facilitate scientific progress in this field.
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}