  Recent advances in deep learning have made it increasingly feasible to
estimate heart rate remotely in smart environments by analyzing videos.
However, a notable limitation of deep learning methods is their heavy reliance
on extensive sets of labeled data for effective training. To address this
issue, self-supervised learning has emerged as a promising avenue. Building on
this, we introduce a solution that utilizes self-supervised contrastive
learning for the estimation of remote photoplethysmography (PPG) and heart rate
monitoring, thereby reducing the dependence on labeled data and enhancing
performance. We propose the use of 3 spatial and 3 temporal augmentations for
training an encoder through a contrastive framework, followed by utilizing the
late-intermediate embeddings of the encoder for remote PPG and heart rate
estimation. Our experiments on two publicly available datasets showcase the
improvement of our proposed approach over several related works as well as
supervised learning baselines, as our results approach the state-of-the-art. We
also perform thorough experiments to showcase the effects of using different
design choices such as the video representation learning method, the
augmentations used in the pre-training stage, and others. We also demonstrate
the robustness of our proposed method over the supervised learning approaches
on reduced amounts of labeled data.

Computer Vision and Pattern Recognition;Machine Learning