This study focuses on integrating optical and radar sensors for human pose estimation. Based on the physical correspondence between the continuous-time micromotion accumulation and pose increment, a single-channel ultra-wideband radar human pose-incremental estimation scheme is proposed. Specifically, by constructing a spatiotemporal incremental estimation network, using spatiotemporal pseudo-3D convolutional and time-domain-dilated convolutional layers to extract spatiotemporal micromotion features step by step, mapping these features to human pose increments within a time period, and combining them with the initial pose values provided by optics, we can realize a 3D pose estimation of the human body. The measured data results show that fusion attitude estimation achieves an estimation error of 5.38 cm in the original action set and can achieve continuous attitude estimation for the period of walking actions. Comparison and ablation experiments with other radar attitude estimation methods demonstrate the advantages of the proposed method.
