Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition:
  A Systematic Study
Ke Xu Jiangtao Wang Hongyuan Zhu Dingchang Zheng
Recently, with the advancement of the Internet of Things (IoT), WiFi
CSI-based HAR has gained increasing attention from academic and industry
communities. By integrating the deep learning technology with CSI-based HAR,
researchers achieve state-of-the-art performance without the need of expert
knowledge. However, the scarcity of labeled CSI data remains the most prominent
challenge when applying deep learning models in the context of CSI-based HAR
due to the privacy and incomprehensibility of CSI-based HAR data. On the other
hand, SSL has emerged as a promising approach for learning meaningful
representations from data without heavy reliance on labeled examples.
Therefore, considerable efforts have been made to address the challenge of
insufficient data in deep learning by leveraging SSL algorithms. In this paper,
we undertake a comprehensive inventory and analysis of the potential held by
different categories of SSL algorithms, including those that have been
previously studied and those that have not yet been explored, within the field.
We provide an in-depth investigation of SSL algorithms in the context of WiFi
CSI-based HAR. We evaluate four categories of SSL algorithms using three
publicly available CSI HAR datasets, each encompassing different tasks and
environmental settings. To ensure relevance to real-world applications, we
design performance metrics that align with specific requirements. Furthermore,
our experimental findings uncover several limitations and blind spots in
existing work, highlighting the barriers that need to be addressed before SSL
can be effectively deployed in real-world WiFi-based HAR applications. Our
results also serve as a practical guideline for industry practitioners and
provide valuable insights for future research endeavors in this field.