SMCTL: Subcarrier Masking Contrastive Transfer Learning for Human Gesture Recognition with Passive Wi-Fi Sensing

Advancements in machine learning, coupled with Wi-Fi sensing using channel state information (CSI), have emerged as a powerful approach for human activity and gesture recognition. Contrary to camera-based systems that rely on capturing images of individuals, passive Wi-Fi sensing provides a non-intrusive method for detecting and interpreting human movements without the need for capturing detailed images. In this paper, subcarrier masking contrastive transfer learning (SMCTL) is proposed for human gesture recognition from CSI measurements. The unsupervised feature extractor is pretrained on a large-scale dataset of CSI measurements. Then, the trained feature extractor and a classifier are fine-tuned end-to-end on a smaller dataset collected from a different environment for cross-domain adaptation. Performance of this method is evaluated for various numbers of fine-tuning samples, for which it outperforms baseline supervised and self-supervised deep learning models.