  Vast quantities of person-generated health data (wearables) are collected but
the process of annotating to feed to machine learning models is impractical.
This paper discusses ways in which self-supervised approaches that use
contrastive losses, such as SimCLR and BYOL, previously applied to the vision
domain, can be applied to high-dimensional health signals for downstream
classification tasks of various diseases spanning sleep, heart, and metabolic
conditions. To this end, we adapt the data augmentation step and the overall
architecture to suit the temporal nature of the data (wearable traces) and
evaluate on 5 downstream tasks by comparing other state-of-the-art methods
including supervised learning and an adversarial unsupervised representation
learning method. We show that SimCLR outperforms the adversarial method and a
fully-supervised method in the majority of the downstream evaluation tasks, and
that all self-supervised methods outperform the fully-supervised methods. This
work provides a comprehensive benchmark for contrastive methods applied to the
wearable time-series domain, showing the promise of task-agnostic
representations for downstream clinical outcomes.
