用视觉数据+物理模型，无接触估算人体多体系统关节力矩。
An Opticalmechanics Framework for Dynamic Estimation of Multibody Systems
- 结合图像测量与机械模型，通过遗传算法优化反推未知力矩。
- 腕关节力矩估计误差仅0.46牛·米，预测角速度误差为0.006弧度/秒。
- 适合无传感器、难布署测力设备的动态分析场景。
传统人体动力学分析常依赖接触式力/力矩传感器及受控实验环境。本文提出一种光学力学一体化框架，用于多体系统动态估计：构建约束多体模型描述系统动力学，以图像测量的运动学量作为非接触输入进行动态推估。通过遗传算法优化，最小化模型预测与图像测量运动学量之间的差异，识别未知关节力矩。在气浮平台上实验验证显示，基于图像数据估算的腕关节力矩均绝对误差为0.46牛·米（相较传感器测量）；前向预测测试中，模型预测角速度均绝对误差为0.006弧度/秒，优于图像测量结果。该研究展示了图像测量与力学建模结合在难以直接测力的场景中实现非接触动态估计的潜力。
原文摘要 · Abstract (English)
Conventional dynamics analysis of the human body is often constrained by the need for contact force and torque sensors and controlled laboratory environments. To address this issue, this study proposes an opticalmechanics kinematic-dynamic integrated estimation framework for multibody systems. Specifically, a constrained multibody model is established to describe the system dynamics, while image-measured kinematic quantities are used as non contact inputs for dynamic estimation. The unknown joint torque is then identified through a genetic-algorithm based optimization by minimizing the discrepancy between model-predicted and image-measured kinematic quan tities. Experimental validation on an air-bearing platform showed that the wrist joint torque estimated from image data achieved a mean absolute error of 0.46 Nm compared with sensor measurements. In the forward prediction test, the model-predicted angular velocity achieved a mean absolute error of 0.006 rad/s relative to the image-measured results. This study demonstrates the potential of combining image measurement and mechanical modeling for non-contact dynamic estimation in scenarios where direct force and torque measurement is difficult.
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