A Flexible Framework for Design and Validation of rPPG Methods

Over the past decade, Remote Photoplethysmography (rPPG) has emerged as an unobtrusive alternative to wearable sensors for measuring physiological signals, such as heart rate. Despite advancements, its real-world scalability remains limited due to the absence of standardized benchmark methods for validation. This lack of standardization complicates proper comparisons between different approaches, creating inconsistencies in performance evaluation. To address this, we conducted a comprehensive review of recent rPPG methods, analyzing their pre- and post-processing algorithms, validation procedures, benchmark algorithms, datasets, evaluation metrics, data segmentation, and reported results. Our findings demonstrate significant variability in the reported Mean Absolute Error (MAE) of benchmark rPPG methods applied to the same public datasets, confirming the challenge of inconsistent evaluation. By examining the original implementations of established benchmark methods, we developed a flexible framework that optimally selects pre- and post-processing algorithms through an exhaustive search. Applying this framework to benchmark algorithms across three public datasets, we found that 80% of the refined methods ranked within the top 25th percentile in MAE, RMSE, and PCC, with 60% surpassing the highest reported accuracies. These refined methods provide a more rigorous foundation for evaluating novel rPPG techniques, addressing the standardization gap in the field. The codebase for this framework (frPPG) is available at [<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Building-Robotics-Lab/flexible_rPPG</uri>], offering a valuable tool for designing and benchmarking rPPG methods against the best-performing algorithms on a given dataset.