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

TL;DR

VST-Pose is a novel WiFi-based human pose estimation framework that uses a spatiotemporal attention network with velocity modeling to achieve high accuracy and robustness in indoor environments.

Contribution

The paper introduces VST-Pose, a new deep learning model with a dual-stream spatiotemporal attention backbone and velocity modeling for improved WiFi-based pose estimation.

Findings

Achieves 92.2% accuracy on PCK@50 metric.

Outperforms existing methods by 8.3% in PCK@50.

Demonstrates robustness on public datasets.

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.