  Self-supervised learning (SSL) for WiFi-based human activity recognition
(HAR) holds great promise due to its ability to address the challenge of
insufficient labeled data. However, directly transplanting SSL algorithms,
especially contrastive learning, originally designed for other domains to CSI
data, often fails to achieve the expected performance. We attribute this issue
to the inappropriate alignment criteria, which disrupt the semantic distance
consistency between the feature space and the input space. To address this
challenge, we introduce \textbf{A}ntenna \textbf{R}esponse \textbf{C}onsistency
(ARC) as a solution to define proper alignment criteria. ARC is designed to
retain semantic information from the input space while introducing robustness
to real-world noise. Moreover, we substantiate the effectiveness of ARC through
a comprehensive set of experiments, demonstrating its capability to enhance the
performance of self-supervised learning for WiFi-based HAR by achieving an
increase of over 5\% in accuracy in most cases and achieving a best accuracy of
94.97\%.
