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Using computed muscle control to generate forward dynamic simulations of human walking from experimental data.

机译:使用计算的肌肉控制从实验数据生成人类步行的前向动态模拟。

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The objective of this study was to develop an efficient methodology for generating muscle-actuated simulations of human walking that closely reproduce experimental measures of kinematics and ground reaction forces. We first introduce a residual elimination algorithm (REA) to compute pelvis and low back kinematic trajectories that ensure consistency between whole-body dynamics and measured ground reactions. We then use a computed muscle control (CMC) algorithm to vary muscle excitations to track experimental joint kinematics within a forward dynamic simulation. CMC explicitly accounts for delays in muscle force production resulting from activation and contraction dynamics while using a general static optimization framework to resolve muscle redundancy. CMC was used to compute muscle excitation patterns that drove a 21-degrees-of-freedom, 92 muscle model to track experimental gait data of 10 healthy young adults. Simulated joint kinematics closely tracked experimental quantities (mean root-mean-squared errors generally less than 1 degrees), and the time histories of muscle activations were similar to electromyographic recordings. A simulation of a half-cycle of gait could be generated using approximately 30 min of computer processing time. The speed and accuracy of REA and CMC make it practical to generate subject-specific simulations of gait.
机译:这项研究的目的是开发一种有效的方法来生成人类行走的肌肉致动模拟,该模拟紧密地再现运动学和地面反作用力的实验测量值。我们首先介绍一种残差消除算法(REA),以计算骨盆和下背部运动轨迹,以确保全身动力学与实测地面反应之间的一致性。然后,我们使用计算的肌肉控制(CMC)算法来改变肌肉的兴奋度,以跟踪前向动态仿真中的实验关节运动学。 CMC明确考虑了激活和收缩动力学导致的肌肉力量产生延迟,同时使用通用的静态优化框架来解决肌肉冗余。 CMC用于计算肌肉兴奋模式,该模式驱动21自由度,92肌肉模型来跟踪10个健康年轻人的实验步态数据。模拟的关节运动学密切跟踪实验量(平均均方根误差通常小于1度),并且肌肉激活的时间历史与肌电图记录相似。半步态的模拟可以使用大约30分钟的计算机处理时间来生成。 REA和CMC的速度和准确性使其可用于生成特定于对象的步态模拟。

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