随着IEEE 802.11bf标准的发布，WiFi感知技术已从学术研究走向工业应用。针对现有的基于 WiFi的人体活动检测系统往往依赖于较强假设约束问题，从如何充分利用无标签 CSI样本出发，设计了一种适用于WiFi感知领域的自监督模型CPCC-Fi。模型在对比学习思想的基础上首先使用序列数据增强生成不同视图的无标记CSI样本；然后通过自监督学习获取CSI序列内在表示特征；再通过少量标记样本对模型进行微调，最后即可实现下游人体活动的有效感知和识别。在自采和公开数据集上的相关实验结果表明，与CNN+Linear、CNN+Transformer+Linear和TS-TCC相比，CPCC-Fi模型的各项性能均有所提升。


With the release of the IEEE 802.11bf standard, WiFi sensing technology has transitioned from academic research to industrial applications. Addressing the issue that existing WiFi-based human activity detection systems often rely on strong assumption constraints, this paper proposes a self- supervised model, CPCC-Fi, tailored for the field of WiFi sensing, starting from how to fully utilize unlabeled channel state information (CSI) samples. Based on the idea of contrastive learning, the model first employs sequential data augmentation to generate unlabeled CSI samples with different views. Then  it acquires the intrinsic representation features of the CSI sequences through self-supervised learning. After fine-tuning the model with a small number of labeled samples, effective perception and recognition of downstream human activities can be achieved. Relevant experiments conducted on both self-collected and public datasets demonstrate that the CPCC-Fi model outperforms CNN+Linear, CNN+Transformer+Linear, and TS-TCC in terms of performance.


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