From Point to Space: 3D Moving Human Pose Estimation Using Commodity WiFi
This work addresses the limitation of previous WiFi-based pose estimation systems, enabling 3D pose reconstruction for people moving throughout a space, which is crucial for pervasive human-computer interaction and health monitoring applications.
The paper introduces Wi-Mose, a novel system for 3D moving human pose estimation using commodity WiFi signals. It achieves key-point localization with 29.7mm P-MPJPE in Line of Sight and 37.8mm P-MPJPE in Non-Line of Sight scenarios, outperforming existing state-of-the-art methods.
In this paper, we present Wi-Mose, the first 3D moving human pose estimation system using commodity WiFi. Previous WiFi-based works have achieved 2D and 3D pose estimation. These solutions either capture poses from one perspective or construct poses of people who are at a fixed point, preventing their wide adoption in daily scenarios. To reconstruct 3D poses of people who move throughout the space rather than a fixed point, we fuse the amplitude and phase into Channel State Information (CSI) images which can provide both pose and position information. Besides, we design a neural network to extract features that are only associated with poses from CSI images and then convert the features into key-point coordinates. Experimental results show that Wi-Mose can localize key-point with 29.7mm and 37.8mm Procrustes analysis Mean Per Joint Position Error (P-MPJPE) in the Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios, respectively, achieving higher performance than the state-of-the-art method. The results indicate that Wi-Mose can capture high-precision 3D human poses throughout the space.