2023/05/24 by Nathan Vance, Vance, Nathan, Jeremy Speth +5
Engineering · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #ECG Monitoring and Analysis #FOS: Computer and information sciences #Hemodynamic Monitoring and Therapy #Non-Invasive Vital Sign Monitoring
paper · pdf · doi:10.48550/arxiv.2305.15199
openalex publication_date 2023/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Remote Photoplethysmography (rPPG), or the remote monitoring of a subject's heart rate using a camera, has seen a shift from handcrafted techniques to deep learning models. While current solutions offer substantial performance gains, we show that these models tend to learn a bias to pulse wave features inherent to the training dataset. We develop augmentations to mitigate this learned bias by expanding both the range and variability of heart rates that the model sees while training, resulting in improved model convergence when training and cross-dataset generalization at test time. Through a 3-way cross dataset analysis we demonstrate a reduction in mean absolute error from over 13 beats per minute to below 3 beats per minute. We compare our method with other recent rPPG systems, finding similar performance under a variety of evaluation parameters.