VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation

• URL: http://arxiv.org/abs/2507.09672v1
• Date: Sun, 13 Jul 2025 15:11:18 GMT
• Title: VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation
• Authors: Xinyu Zhang, Zhonghao Ye, Jingwei Zhang, Xiang Tian, Zhisheng Liang, Shipeng Yu,
• Abstract summary: VST-Pose is a novel framework for accurate pose estimation using WiFi channel state information.<n>Our method achieves 92.2% accuracy on the PCK@50 metric, outperforming existing methods by 8.3% in PCK@50 on the self-collected dataset.<n>The proposed system provides a reliable and privacy-aware solution for continuous human motion analysis in indoor environments.
• Score: 10.371863139473456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: WiFi-based human pose estimation has emerged as a promising non-visual alternative approaches due to its pene-trability and privacy advantages. This paper presents VST-Pose, a novel deep learning framework for accurate and continuous pose estimation using WiFi channel state information. The proposed method introduces ViSTA-Former, a spatiotemporal attention backbone with dual-stream architecture that adopts a dual-stream architecture to separately capture temporal dependencies and structural relationships among body joints. To enhance sensitivity to subtle human motions, a velocity modeling branch is integrated into the framework, which learns short-term keypoint dis-placement patterns and improves fine-grained motion representation. We construct a 2D pose dataset specifically designed for smart home care scenarios and demonstrate that our method achieves 92.2% accuracy on the PCK@50 metric, outperforming existing methods by 8.3% in PCK@50 on the self-collected dataset. Further evaluation on the public MMFi dataset confirms the model's robustness and effectiveness in 3D pose estimation tasks. The proposed system provides a reliable and privacy-aware solution for continuous human motion analysis in indoor environments. Our codes are available in https://github.com/CarmenQing/VST-Pose.

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