DSA-Net: A Dual-Path Spatial-Temporal Attention Network for WiFi-Based Human Activity Recognition

WiFi-based Human Activity Recognition (HAR) enables privacy-preserving, device-free motion detection using Channel State Information (CSI) from commodity devices. However, CSI's low resolution and noise complicate spatiotemporal feature extraction. We propose DSA-Net, a Dual-Path Spatial-Temporal Attention Network tailored to CSI-based HAR. It combines a slow-fast temporal pathway with cross-spatial attention to capture fine-grained and long-range dependencies, while a Transformer-based fusion module adaptively integrates spatiotemporal features. Evaluated on the Widar3.0 dataset, DSA-Net surpasses Vision Transformer (ViT) baselines by 18.91 percentage points, achieving superior accuracy with low computational overhead. Our results demonstrate DSA-Nets potential for scalable, real-time activity recognition in IoT and smart environments.