Two optimization techniques, static optimization (SO) and computed muscle control (CMC), are often used in OpenSim to estimate the muscle activations and forces responsible for movement. Although differences between SO and CMC muscle function have been reported, the accuracy of each technique and the combined effect of optimization and model choice on simulated muscle function is unclear. The purpose of this study was to quantitatively compare the SO and CMC estimates of muscle activations and forces during gait with the experimental data in the Gait2392 and Full Body Running models. In OpenSim (version 3.1), muscle function during gait was estimated using SO and CMC in 6 subjects in each model and validated against experimental muscle activations and joint torques. Experimental and simulated activation agreement was sensitive to optimization technique for the soleus and tibialis anterior. Knee extension torque error was greater with CMC than SO. Muscle forces, activations, and co-contraction indices tended to be higher with CMC and more sensitive to model choice. CMC’s inclusion of passive muscle forces, muscle activation-contraction dynamics, and a proportional-derivative controller to track kinematics contributes to these differences. Model and optimization technique choices should be validated using experimental activations collected simultaneously with the data used to generate the simulation.

OpenSim 中经常使用静态优化 (SO) 和计算肌肉控制 (CMC) 两种优化技术来估计肌肉激活和负责运动的力。尽管 SO 和 CMC 肌肉功能之间的差异已有报道，但每种技术的准确性以及优化和模型选择对模拟肌肉功能的综合影响尚不清楚。本研究的目的是将步态期间肌肉激活和力量的 SO 和 CMC 估计值与 Gait2392 和全身跑步模型中的实验数据进行定量比较。在 OpenSim（版本 3.1）中，每个模型中的 6 名受试者使用 SO 和 CMC 估计步态期间的肌肉功能，并根据实验肌肉激活和关节扭矩进行验证。实验和模拟激活一致性对比目鱼肌和胫骨前肌的优化技术敏感。CMC 的膝关节伸展扭矩误差比 SO 更大。CMC 的肌肉力量、激活和共同收缩指数往往更高，并且对模型选择更敏感。CMC 包含被动肌肉力、肌肉激活-收缩动力学以及用于跟踪运动学的比例微分控制器，从而导致了这些差异。应使用与用于生成模拟的数据同时收集的实验激活来验证模型和优化技术的选择。