Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training

TL;DR

This paper introduces a self-supervised contrastive learning approach for remote heart rate monitoring from videos, significantly reducing labeled data requirements while achieving near state-of-the-art performance.

Contribution

The paper presents a novel self-supervised contrastive learning framework utilizing spatial and temporal augmentations for remote PPG and heart rate estimation from videos.

Findings

Improves heart rate estimation accuracy over related works and supervised baselines.

Demonstrates robustness with less labeled data.

Approaches state-of-the-art performance.

Abstract

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.