Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB.
Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB.
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作者：
Lee LF;Umberger BR
计算机建模、仿真和优化是生物力学研究中使用越来越多的强大工具。动态优化可以分为数据跟踪问题和预测问题。数据跟踪方法已被广泛用于解决临床相关的人体运动问题。预测方法也有很大的希望，但在临床应用中的使用有限。增强的软件工具将促进预测性肌肉骨骼模拟在临床相关研究中的应用。开源软件OpenSim提供了生成跟踪模拟的工具，但不提供预测模拟。然而，OpenSim包括一个广泛的应用程序编程接口，允许扩展其功能与脚本语言，如MATLAB。在这里介绍的工作中，我们结合联合收割机的MATLAB提供的计算工具与肌肉骨骼建模功能的OpenSim创建一个框架，用于生成预测模拟的肌肉骨骼运动的基础上直接配置最优控制技术。在许多情况下，直接配置法可以用来解决最优控制问题，大大快于传统的射击方法。分别采用简单的1自由度肌肉骨骼模型和人体下肢模型解决了周期性和离散性运动问题。使用开源IPOPT求解器可以在合理的时间内（几秒钟到1-2小时）解决这些问题。这些问题也可以使用MATLAB中包含的fmincon求解器来解决，但是除了最小的问题之外，所有问题的计算时间都过长。IPOPT的性能优势主要是通过利用约束雅可比矩阵中的稀疏性来获得的。这里提出的框架提供了一个强大而灵活的方法，使用OpenSim和MATLAB生成肌肉骨骼运动的最佳控制仿真。这将使研究人员更容易使用预测模拟作为工具来解决限制人类移动性的临床条件。
Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1–2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This should allow researchers to more readily use predictive simulation as a tool to address clinical conditions that limit human mobility.