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\%.

翻译：基于WiFi的人体活动识别（HAR）中的自监督学习（SSL）因其解决标注数据不足问题的潜力而备受关注。然而，直接移植其他领域（尤其是对比学习）为CSI数据设计的SSL算法往往无法达到预期性能。我们认为这一问题源于不当的对比标准，它破坏了特征空间与输入空间之间的语义距离一致性。为解决这一挑战，我们提出天线响应一致性（ARC）作为定义适当对比标准的解决方案。ARC旨在保留输入空间中的语义信息，同时增强对现实噪声的鲁棒性。此外，我们通过一系列实验验证了ARC的有效性，证明其能够提升WiFi-HAR自监督学习的性能，在大多数情况下准确率提升超过5%，并达到94.97%的最佳准确率。