Lin Yi-Chung, Pandy Marcus G
Department of Mechanical Engineering, University of Melbourne, Victoria 3010, Australia.
Department of Mechanical Engineering, University of Melbourne, Victoria 3010, Australia.
J Biomech. 2017 Jul 5;59:1-8. doi: 10.1016/j.jbiomech.2017.04.038. Epub 2017 May 19.
The aim of this study was to perform full-body three-dimensional (3D) dynamic optimization simulations of human locomotion by driving a neuromusculoskeletal model toward in vivo measurements of body-segmental kinematics and ground reaction forces. Gait data were recorded from 5 healthy participants who walked at their preferred speeds and ran at 2m/s. Participant-specific data-tracking dynamic optimization solutions were generated for one stride cycle using direct collocation in tandem with an OpenSim-MATLAB interface. The body was represented as a 12-segment, 21-degree-of-freedom skeleton actuated by 66 muscle-tendon units. Foot-ground interaction was simulated using six contact spheres under each foot. The dynamic optimization problem was to find the set of muscle excitations needed to reproduce 3D measurements of body-segmental motions and ground reaction forces while minimizing the time integral of muscle activations squared. Direct collocation took on average 2.7±1.0h and 2.2±1.6h of CPU time, respectively, to solve the optimization problems for walking and running. Model-computed kinematics and foot-ground forces were in good agreement with corresponding experimental data while the calculated muscle excitation patterns were consistent with measured EMG activity. The results demonstrate the feasibility of implementing direct collocation on a detailed neuromusculoskeletal model with foot-ground contact to accurately and efficiently generate 3D data-tracking dynamic optimization simulations of human locomotion. The proposed method offers a viable tool for creating feasible initial guesses needed to perform predictive simulations of movement using dynamic optimization theory. The source code for implementing the model and computational algorithm may be downloaded at http://simtk.org/home/datatracking.
本研究的目的是通过驱动神经肌肉骨骼模型以使其符合体内身体节段运动学和地面反作用力的测量结果，来对人体运动进行全身三维（3D）动态优化模拟。记录了5名健康参与者以其偏好速度行走以及以2m/s跑步时的步态数据。使用直接配置法并结合OpenSim-MATLAB接口，针对一个步幅周期生成了参与者特定的数据跟踪动态优化解决方案。身体被表示为一个由66个肌肉-肌腱单元驱动的12节段、21自由度的骨架。使用每只脚下的六个接触球来模拟脚与地面的相互作用。动态优化问题是要找到一组肌肉兴奋信号，以便在最小化肌肉激活平方的时间积分的同时，重现身体节段运动和地面反作用力的三维测量结果。直接配置法分别平均花费2.7±1.0小时和2.2±1.6小时的CPU时间来解决行走和跑步的优化问题。模型计算得到的运动学和脚与地面的力与相应的实验数据高度吻合，而计算得到的肌肉兴奋模式与测得的肌电图活动一致。结果表明，在具有脚与地面接触的详细神经肌肉骨骼模型上实施直接配置法，以准确高效地生成人体运动的三维数据跟踪动态优化模拟是可行的。所提出的方法为使用动态优化理论进行运动预测模拟创建可行的初始猜测提供了一个可行的工具。实现该模型和计算算法的源代码可从http://simtk.org/home/datatracking下载。
