TRUST: Time-Domain Residual Unsupervised Stability Technique for Improved Heart Rate Estimation

Camera-based estimation of vital signs is a promising method for non-contact health monitoring, which analyzes minute changes in video data. However, the creation of accurate models for this task is challenging due to the scarcity of datasets that possess synchronized vital sign recordings. Our research enhances an existing non-contrastive unsupervised learning technique for extracting rPPG signals, which does not necessitate ground-truth signals during the training process. We have incorporated new time-domain loss functions and added a feature stabilization block to improve the model's stability and accuracy in detecting low-level features. Additionally, we have devised a metric to evaluate the feature instability in the model's final layer. Our experiments on four public datasets demonstrate that our method surpasses the performance of current state-of-the-art methods. These advancements make our approach a significant breakthrough in the development of scalable deep-learning models for camera-based heart-rate estimation.