  Recent advances in supervised deep learning methods are enabling remote
measurements of photoplethysmography-based physiological signals using facial
videos. The performance of these supervised methods, however, are dependent on
the availability of large labelled data. Contrastive learning as a
self-supervised method has recently achieved state-of-the-art performances in
learning representative data features by maximising mutual information between
different augmented views. However, existing data augmentation techniques for
contrastive learning are not designed to learn physiological signals from
videos and often fail when there are complicated noise and subtle and periodic
colour or shape variations between video frames. To address these problems, we
present a novel self-supervised spatiotemporal learning framework for remote
physiological signal representation learning, where there is a lack of labelled
training data. Firstly, we propose a landmark-based spatial augmentation that
splits the face into several informative parts based on the Shafer dichromatic
reflection model to characterise subtle skin colour fluctuations. We also
formulate a sparsity-based temporal augmentation exploiting Nyquist-Shannon
sampling theorem to effectively capture periodic temporal changes by modelling
physiological signal features. Furthermore, we introduce a constrained
spatiotemporal loss which generates pseudo-labels for augmented video clips. It
is used to regulate the training process and handle complicated noise. We
evaluated our framework on 3 public datasets and demonstrated superior
performances than other self-supervised methods and achieved competitive
accuracy compared to the state-of-the-art supervised methods.
