Subtle periodic signals such as blood volume pulse and respiration can be extracted from RGB video, enabling remote health monitoring at low cost. Advancements in remote pulse estimation -- or remote photoplethysmography (rPPG) -- are currently driven by deep learning solutions. However, modern approaches are trained and evaluated on benchmark datasets with associated ground truth from contact-PPG sensors. We present the first non-contrastive unsupervised learning framework for signal regression to break free from the constraints of labelled video data. With minimal assumptions of periodicity and finite bandwidth, our approach is capable of discovering the blood volume pulse directly from unlabelled videos. We find that encouraging sparse power spectra within normal physiological bandlimits and variance over batches of power spectra is sufficient for learning visual features of periodic signals. We perform the first experiments utilizing unlabelled video data not specifically created for rPPG to train robust pulse rate estimators. Given the limited inductive biases and impressive empirical results, the approach is theoretically capable of discovering other periodic signals from video, enabling multiple physiological measurements without the need for ground truth signals. Codes to fully reproduce the experiments are made available along with the paper.

翻译：微妙周期信号（如血容量脉搏和呼吸）可从RGB视频中提取，从而实现低成本远程健康监测。远程脉搏估计（即远程光电容积描记术（rPPG））的最新进展主要由深度学习解决方案推动。然而，当前方法均在带有接触式PPG传感器金标准数据的基准数据集上进行训练与评估。我们首次提出基于非对比性无监督学习的信号回归框架，以摆脱标注视频数据的约束。该方法仅基于周期性和有限带宽的最小假设，即可直接从无标注视频中发现血容量脉搏信号。我们发现，在正常生理频带内鼓励稀疏功率谱并增强各批次功率谱方差的策略，足以学习周期信号的视觉特征。我们首次利用非专门为rPPG创建的无标注视频数据训练稳健的脉搏率估计器。鉴于其有限的归纳偏置与令人瞩目的实证结果，该方法理论上可从视频中发现其他周期信号，从而在无需金标准信号的情况下实现多种生理测量。本文随附可完整复现实验的代码。