Imbalanced Semi-Supervised Learning for WiFi Gesture Recognition via Dynamic Threshold-Based Spatio-Temporal Attention Networks

Abstract

WiFi sensing advancements facilitate the capture of human gestures from wireless signals, ensuring both privacy preservation and robustness under low-light conditions. Deep learning-based WiFi Human Gesture Recognition (HGR) demonstrates remarkable performance in handling complex gestures. To reduce labeling efforts, recent years have seen the emergence of semi-supervised WiFi HGR, leveraging massive amounts of unlabeled data. However, existing semi-supervised schemes often assume a balanced class distribution and utilize a fixed threshold for selecting pseudo-labels of unlabeled samples, leading to low performance for minority classes and decreased model generalization on real-world imbalanced datasets. To address this issue, we propose a novel semi-supervised WiFi HGR approach with dynamic pseudo-labeling thresholds to handle imbalanced class distribution, incorporating Spatial-Temporal Attention (STA) networks. Unlike using a fixed threshold for all unlabeled samples, our design implements class-independent thresholds for different classes, dynamically adjusting them by encoding pseudo-label distribution during training. To emphasize critical features in informative areas within the WiFi signals, we incorporate both spatial self-attention and temporal attention mechanisms to dynamically learn salient features and identify pivotal frames, respectively. Moreover, we introduce adaptive WiFi data augmentations that propel the semi-supervised framework and enhance model robustness. Experimental results on the Widar3.0 dataset reveal that our approach outperforms existing semi-supervised methods by large margins in accuracy, effectively mitigating imbalanced bias and enhancing model generalization.

Citation

Q. Feng, C. Duan, J. Xue, C. Li, F. Huang, X. Zhang, J. Weng, and P. Yu, “Imbalanced Semi-Supervised Learning for WiFi Gesture Recognition via Dynamic Threshold-Based Spatio-Temporal Attention Networks,” IEEE Transactions on Mobile Computing (TMC) , vol. 25, no. 1, pp. 483-499, 2026.

中文引用（GB/T 7714）

Feng Q, Duan C, Xue J, et al. Imbalanced Semi-Supervised Learning for WiFi Gesture Recognition via Dynamic Threshold-Based Spatio-Temporal Attention Networks[J]. IEEE Transactions on Mobile Computing (TMC), 2026, 25(1): 483-499.