In this study, the three-dimensional (3D) asymmetric maximum weight lifting is predicted using an inverse-dynamics-based optimization method considering dynamic joint torque limits. The dynamic joint torque limits are functions of joint angles and angular velocities, and imposed on the hip, knee, ankle, wrist, elbow, shoulder, and lumbar spine joints. The 3D model has 40 degrees of freedom (DOFs) including 34 physical revolute joints and 6 global joints. A multi-objective optimization (MOO) problem is solved by simultaneously maximizing box weight and minimizing the sum of joint torque squares. A total of 12 male subjects were recruited to conduct maximum weight box lifting using squat-lifting strategy. Finally, the predicted lifting motion, ground reaction forces, and maximum lifting weight are validated with the experimental data. The prediction results agree well with the experimental data and the model’s predictive capability is demonstrated. This is the first study that uses MOO to predict maximum lifting weight and 3D asymmetric lifting motion while considering dynamic joint torque limits. The proposed method has the potential to prevent individuals’ risk of injury for lifting.

在这项研究中，考虑到动态关节扭矩限制，使用基于逆动力学的优化方法来预测三维 (3D) 非对称最大举重。动态关节扭矩限制是关节角度和角速度的函数，并施加在髋关节、膝关节、踝关节、腕关节、肘关节、肩关节和腰椎关节上。3D 模型具有 40 个自由度 (DOF)，包括 34 个物理旋转关节和 6 个全局关节。多目标优化 (MOO) 问题通过同时最大化盒子重量和最小化关节扭矩平方和来解决。共招募了 12 名男性受试者，使用蹲举策略进行最大重量的举重训练。最后，通过实验数据验证了预测的提升运动、地面反作用力和最大提升重量。预测结果与实验数据吻合良好，证明了模型的预测能力。这是第一项使用 MOO 预测最大提升重量和 3D 非对称提升运动同时考虑动态关节扭矩限制的研究。所提出的方法有可能防止个人因举重而受伤的风险。