  Subtle periodic signals, such as blood volume pulse and respiration, can be
extracted from RGB video, enabling noncontact 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 ground truth
from contact-PPG sensors. We present the first non-contrastive unsupervised
learning framework for signal regression to mitigate the need for labelled
video data. With minimal assumptions of periodicity and finite bandwidth, our
approach discovers 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, we
successfully applied the same approach to camera-based respiration by changing
the bandlimits of the target signal. This shows that the approach is general
enough for unsupervised learning of bandlimited quasi-periodic signals from
different domains. Furthermore, we show that the framework is effective for
finetuning models on unlabelled video from a single subject, allowing for
personalized and adaptive signal regressors.
