Based Remote Physiological

Based remote physiological measurement uses cameras to non-invasively monitor vital signs like heart rate, respiration, and blood oxygen saturation, aiming to provide convenient and accessible healthcare monitoring. Current research focuses on improving the accuracy and robustness of deep learning models, particularly convolutional neural networks and transformers, often incorporating techniques like contrastive learning and self-supervised learning to address data limitations and biases, especially concerning skin tone variations. These advancements hold significant promise for expanding healthcare access, particularly in remote or resource-constrained settings, and for enabling large-scale population health studies.