Sreenivasa Manish, Millard Matthew, Kingma Idsart, van Dieën Jaap H, Mombaur Katja
School of Mechanical, Materials, Mechatronic & Biomedical Engineering, University of Wollongong, NSW 2522, Australia; Optimization, Robotics & Biomechanics, Institute of Computer Engineering, Heidelberg University, Germany.
Optimization, Robotics & Biomechanics, Institute of Computer Engineering, Heidelberg University, Germany.
J Biomech. 2018 Sep 10;78:118-125. doi: 10.1016/j.jbiomech.2018.07.028. Epub 2018 Jul 29.
Computational models of the human body coupled with optimization can be used to predict the influence of variables that cannot be experimentally manipulated. Here, we present a study that predicts the motion of the human body while lifting a box, as a function of flexibility of the hip and lumbar joints in the sagittal plane. We modeled the human body in the sagittal plane with joints actuated by pairs of agonist-antagonist muscle torque generators, and a passive hamstring muscle. The characteristics of a stiff, average and flexible person were represented by co-varying the lumbar range-of-motion, lumbar passive extensor-torque and the hamstring passive muscle-force. We used optimal control to solve for motions that simulated lifting a 10 kg box from a 0.3 m height. The solution minimized the total sum of the normalized squared active and passive muscle torques and the normalized passive hamstring muscle forces, over the duration of the motion. The predicted motion of the average lifter agreed well with experimental data in the literature. The change in model flexibility affected the predicted joint angles, with the stiffer models flexing more at the hip and knee, and less at the lumbar joint, to complete the lift. Stiffer models produced similar passive lumbar torque and higher hamstring muscle force components than the more flexible models. The variation between the motion characteristics of the models suggest that flexibility may play an important role in determining lifting technique.
结合优化技术的人体计算模型可用于预测无法通过实验操作的变量的影响。在此，我们展示了一项研究，该研究预测了人体在提起箱子时的运动，此运动是矢状面内髋关节和腰椎关节灵活性的函数。我们在矢状面内对人体进行建模，关节由成对的主动肌 - 拮抗肌扭矩发生器以及一条被动绳肌驱动。通过改变腰椎活动范围、腰椎被动伸肌扭矩和绳肌被动肌力来体现僵硬、普通和灵活人群的特征。我们使用最优控制来求解模拟从0.3米高度提起10千克箱子的运动。该解决方案在运动持续时间内使归一化平方主动肌和被动肌扭矩以及归一化被动绳肌肌力的总和最小化。普通举重者的预测运动与文献中的实验数据吻合良好。模型灵活性的变化影响了预测的关节角度，较僵硬的模型在髋关节和膝关节处弯曲更多，而在腰椎关节处弯曲更少，以完成提起动作。较僵硬的模型比较灵活的模型产生了相似的被动腰椎扭矩和更高的绳肌肌力分量。模型运动特征之间的差异表明，灵活性可能在确定举重技术方面发挥重要作用。
