Abstract: 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.

Key words: self-supervised learning, channel state information, human activity recognition, representation learning