A Spatiotemporal Attention Network for mmWave-Based 3-D Human Skeleton Estimation

This article presents a spatiotemporal attention framework for radar-based human pose estimation (STAR-Pose), a unified model for estimating 3-D skeletal coordinates directly from millimeter-wave (mmWave) radar point clouds. The framework integrates PointNet++ for spatial feature extraction and a bidirectional long short-term memory (BiLSTM) module enhanced by attention pooling for temporal modeling. Data were collected using a selfdeveloped BHYY_MMW6044 radar operating in the 59–64-GHz band, capturing dynamic human motion in diverse scenarios. Comprehensive experiments demonstrate that STAR-Pose achieves an average localization error of 1.76 cm, outperforming existing radar-based baselines. The framework exhibits strong robustness to noisy frames, varying motion speeds, and cross-subject conditions, while maintaining stable accuracy under occlusion and multipath interference. Overall, STAR-Pose provides a reliable and privacy-preserving approach for human pose estimation with mmWave radar, paving the way for intelligent sensing applications in smart healthcare, ambient monitoring, and human–computer interaction.