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.

本研究针对工业国家的成年人在工作、开车和日常生活中长时间久坐的情况，通过使用毫米波雷达来表征坐姿上半身人体姿态，提出了一个名为SUPER的框架，并利用近距离的双毫米波雷达对其进行估计。通过提出的新型掩蔽算法，将雷达数据相干地融合，生成具有互补信息的强度和多普勒点云，用于处理高运动但雷达截面积小的区域（例如上肢）和低运动但雷达截面积大的区域（例如躯干）。通过轻量级神经网络提取上半身的全局和局部特征，并输出Skinned Multi-Person Linear (SMPL)模型的姿势参数。在来自多个被试的不同运动序列上进行了大量的留一主题实验，结果表明SUPER比最先进的基线方法提高了30-184%。我们还演示了它在一个简单的下游任务中对手物交互的实用性。

基于毫米波雷达的坐姿上半身姿态估计