An image-processing toolkit for remote photoplethysmography

Abstract

Objective. Image-processing-based remote photoplethysmography algorithms are usually composed of steps where different methods are used, and often, researchers perform these steps using methods that are not necessarily the best for their application. With our toolkit, we want to provide easy and fast access to different state-of-the-art methods for the most common image-processing steps in remote photoplethysmography algorithms. Methods. Our toolkit was programmed in Python and was developed with sequential workflow in mind, making it versatile and easy to use in interactive environments. It also includes tools so the users can modify or extend it if they want to, and will be updated as new methods for the different steps are published. Results. Our use case examples and validation show an effective approach and how the toolkit can be used for exhaustive evaluation and ablation studies in a simple way. We also show how choosing different methods can affect the final heart rate estimation accuracy at the cost of computation time. Conclusion. With this toolkit we are providing researchers with a versatile, easy-to-use tool, with access to different methods for the most common steps in remote photoplethysmography algorithms. Significance. Our toolkit is a relevant tool for researchers in the remote photoplethysmography field due to their versatility, ease of use, and adaptability. (It will be available onhttps://github.com/Montyro/rppgtkgithub upon acceptance).