  Self-supervised learning establishes a new paradigm of learning
representations with much fewer or even no label annotations. Recently there
has been remarkable progress on large-scale contrastive learning models which
require substantial computing resources, yet such models are not practically
optimal for small-scale tasks. To fill the gap, we aim to study contrastive
learning on the wearable-based activity recognition task. Specifically, we
conduct an in-depth study of contrastive learning from both algorithmic-level
and task-level perspectives. For algorithmic-level analysis, we decompose
contrastive models into several key components and conduct rigorous
experimental evaluations to better understand the efficacy and rationale behind
contrastive learning. More importantly, for task-level analysis, we show that
the wearable-based signals bring unique challenges and opportunities to
existing contrastive models, which cannot be readily solved by existing
algorithms. Our thorough empirical studies suggest important practices and shed
light on future research challenges. In the meantime, this paper presents an
open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical
tool for researchers. The library is highly modularized and easy to use, which
opens up avenues for exploring novel contrastive models quickly in the future.
