  The emergence of self-supervised learning in the field of wearables-based
human activity recognition (HAR) has opened up opportunities to tackle the most
pressing challenges in the field, namely to exploit unlabeled data to derive
reliable recognition systems for scenarios where only small amounts of labeled
training samples can be collected. As such, self-supervision, i.e., the
paradigm of 'pretrain-then-finetune' has the potential to become a strong
alternative to the predominant end-to-end training approaches, let alone
hand-crafted features for the classic activity recognition chain. Recently a
number of contributions have been made that introduced self-supervised learning
into the field of HAR, including, Multi-task self-supervision, Masked
Reconstruction, CPC, and SimCLR, to name but a few. With the initial success of
these methods, the time has come for a systematic inventory and analysis of the
potential self-supervised learning has for the field. This paper provides
exactly that. We assess the progress of self-supervised HAR research by
introducing a framework that performs a multi-faceted exploration of model
performance. We organize the framework into three dimensions, each containing
three constituent criteria, such that each dimension captures specific aspects
of performance, including the robustness to differing source and target
conditions, the influence of dataset characteristics, and the feature space
characteristics. We utilize this framework to assess seven state-of-the-art
self-supervised methods for HAR, leading to the formulation of insights into
the properties of these techniques and to establish their value towards
learning representations for diverse scenarios.
