AI 中文总结
本研究构建整合全身骨骼模型的端到端雷达姿态估计框架，在11名康复训练受试者的交叉验证中实现多项精准指标，证明从低成本雷达恢复生物力学描述符的可行性，为临床运动分析奠定基础。
AI 中文摘要
基于雷达的人体姿态估计研究多聚焦于改进学习算法，却将人体表示为无约束的关键点坐标。本研究针对被忽视的解剖保真度维度，将全身骨骼模型整合到可微、端到端可训练的基于雷达的姿态估计框架中；其中姿态网络通过正向运动学进行监督，且受试者特定的几何参数需预先拟合。从雷达点云特征预测受试者特定的身体段比例，以缩放生物力学骨骼；运动预测网络将时序雷达序列映射到广义坐标，可微正向运动学将预测的关节角转换为3D位置；接触分类损失促使实现物理上合理的足部-地面交互。在11名执行康复训练的健康受试者的留一受试者交叉验证下，该框架实现的平均每关节位置误差（MPJPE）为6.456±1.759 cm，平均每关节角度误差（MPJAE）为8.083±0.884度，接触分类F1值为0.935±0.009，缩放误差为3.4±1.3%。该概念验证研究证明，在受控实验室环境中，从单个低成本雷达传感器恢复可解释的生物力学描述符具有可行性，这是未来临床运动分析的前提条件。
英文摘要
Radar-based human pose estimation has focused on improving learning algorithms while representing the body as unconstrained keypoint coordinates. We address the underexplored dimension of anatomical fidelity by integrating a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework, in which the pose network is supervised through forward kinematics while subject-specific geometry is fitted beforehand. Subject-specific body segment proportions are predicted from radar point cloud features to scale a biomechanical skeleton. A motion prediction network maps temporal radar sequences to generalized coordinates, and differentiable forward kinematics converts predicted joint angles into 3D positions. A contact classification loss encourages physically plausible foot-ground interaction. Under leave-one-subject-out cross-validation on 11 healthy participants performing rehabilitation exercises, the framework achieves 6.456 +/- 1.759 cm mean per-joint position error (MPJPE), 8.083 +/- 0.884 degrees mean per-joint angle error (MPJAE), 0.935 +/- 0.009 contact classification F1, and 3.4 +/- 1.3 % scaling error. This proof-of-concept study demonstrates the feasibility of recovering interpretable biomechanical descriptors from a single low-cost radar sensor in a controlled laboratory setting, a prerequisite for future clinical motion analysis.
