mRadHPRS: Human Pose Recognition System From Point Clouds Generated Through a Millimeter-Wave Radar

Abstract

This article proposes a human pose recognition (HPR) system based on millimeter-wave (mmWave) radar. It includes an adaptive method for generating 3D point clouds of human bodies, a data augmentation method for sparse point clouds, and an HPR network based on mmWave radar. This study seeks to address a deficiency in current HPR research, which predominantly emphasizes various deep-learning variants and often pays less attention to the accurate extraction of features from radar signals. The proposed system considers factors such as multipath effects in radar echoes and designs data augmentation methods tailored to the sparse distribution characteristics of mmWave radar point clouds. Additionally, a hierarchical point cloud processing network incorporating cross- and self-attention mechanisms is devised to extract human pose features. To evaluate the performance of the proposed model, we constructed a dataset with 10 different postures using mmWave radar. Experimental results demonstrate that the overall accuracy and average classification accuracy achieved by our method are 88.38% and 88.63%, respectively, significantly outperforming the three baseline methods. The system robustness experiment further validates the generalization ability and effectiveness of our approach.