Eskinazi Ilan, Fregly Benjamin J
Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA.
Department of Mechanical Engineering, Rice University, Houston, TX, USA.
Med Eng Phys. 2018 Apr;54:56-64. doi: 10.1016/j.medengphy.2018.02.002. Epub 2018 Mar 2.
Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function.
通过基于梯度的肌肉骨骼模型优化来同时估计肌肉激活、关节接触力和关节运动学,受到计算成本高昂且非平滑的关节接触和肌肉包裹算法的阻碍。我们提出了一个框架,该框架通过并行化和代理建模相结合的方式,同时加快计算速度并消除肌肉力优化中的非平滑源,特别强调一种将关节接触建模为静态分析代理模型的新方法。该方法允许在人体运动的静态和动态优化中有效地引入弹性关节接触模型。我们通过使用经历步态周期的骨盆 - 腿部肌肉骨骼模型进行一次静态优化和一次动态优化来演示该方法。我们观察到,对于静态优化时间框架,收敛时间约为几秒,而对于整个动态优化,收敛时间约为几分钟。所提出的框架可能有助于基于模型的工作,以预测计划的手术或康复干预将如何影响治疗后的关节和肌肉功能。