Efficient Through-Wall Human Pose Reconstruction Using UWB MIMO Radar

In this letter, we introduce ultrawideband (UWB) Pose, a through-wall human pose reconstruction framework using UWB multiple-input--multiple-output (MIMO) radar. Previous radio frequency-based works have achieved two-dimensional (2-D) and 3-D human pose reconstruction in human detection of occlusion scenes. However, these methods are difficult to adapt to the environment and often suffer from high computational complexity. To address this issue, we first construct 3-D radar images of human targets using UWB MIMO radar system, and transform those radar images into discrete 3-D point data. Then, design a lightweight deep learning network to extract human body features from the input point data, finally convert the features into 3-D pose coordinates. The comparative experimental results show that the human pose reconstruction error of our UWB-Pose framework can be as low as 38.84 mm. Importantly, the number of parameters and floating point operations are further reduced to 1.02 M and 2.75 G, to meet the needs in practical deployments and the scope of applicability.