In industrial countries, adults spend a considerable amount of time sedentary each day at work, driving and during activities of daily living. Characterizing the seated upper body human poses using mmWave radars is an important, yet under-studied topic with many applications in human-machine interaction, transportation and road safety. In this work, we devise SUPER, a framework for seated upper body human pose estimation that utilizes dual-mmWave radars in close proximity. A novel masking algorithm is proposed to coherently fuse data from the radars to generate intensity and Doppler point clouds with complementary information for high-motion but small radar cross section areas (e.g., upper extremities) and low-motion but large RCS areas (e.g. torso). A lightweight neural network extracts both global and local features of upper body and output pose parameters for the Skinned Multi-Person Linear (SMPL) model. Extensive leave-one-subject-out experiments on various motion sequences from multiple subjects show that SUPER outperforms a state-of-the-art baseline method by 30 -- 184%. We also demonstrate its utility in a simple downstream task for hand-object interaction.

翻译：在工业化国家，成年人每日在工作、驾驶及日常活动中花费大量时间处于久坐状态。利用毫米波雷达表征坐姿下的人体上半身姿态是一个重要但研究不足的课题，在人机交互、交通运输及道路安全等领域具有广泛应用前景。本研究提出SUPER框架，该框架利用近距离部署的双毫米波雷达实现坐姿人体上半身姿态估计。我们提出一种新颖的掩蔽算法，以相干融合来自双雷达的数据，生成包含互补信息的强度点云与多普勒点云，分别针对高运动但雷达散射截面积较小的区域（如上肢）以及低运动但雷达散射截面积较大的区域（如躯干）。一个轻量级神经网络提取上半身的全局与局部特征，并输出适用于Skinned Multi-Person Linear (SMPL)模型的姿态参数。通过对多名受试者多种运动序列进行的广泛留一受试者交叉验证实验表明，SUPER的性能优于当前最先进的基线方法30%至184%。我们还通过一个简单的手部-物体交互下游任务展示了其实用性。