OpenSim Moco: Musculoskeletal optimal control.
OpenSim Moco: Musculoskeletal optimal control.
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DOI：
10.1371/journal.pcbi.1008493
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发表时间：
2020-12
影响因子：
4.3
通讯作者：
Delp SL
中科院分区：
文献类型：
--
作者：
Dembia CL;Bianco NA;Falisse A;Hicks JL;Delp SL
肌肉骨骼模拟被用于许多不同的应用，从设计可穿戴机器人与人类互动到分析运动障碍患者。在这里，我们介绍OpenSim Moco，这是一个软件工具包，用于优化OpenSim建模和仿真包中内置的肌肉骨骼模型的运动和控制。OpenSim Moco使用直接搭配方法，这种方法通常比其他方法更快，可以处理更多样化的肌肉骨骼模拟问题。Moco将研究人员从执行直接搭配中解放出来——这通常需要广泛的技术专长——并允许他们专注于他们的科学问题。该软件可以处理生物力学家感兴趣的广泛问题，包括运动跟踪，运动预测，参数优化，模型拟合，肌电驱动模拟和设备设计。Moco是第一个处理运动学约束的肌肉骨骼直接搭配工具，它可以建模运动学环路（例如，循环模型）和复杂的解剖学（例如，髌骨运动）。为了展示Moco的能力，我们首先求解了产生观察到的步行运动的肌肉活动，同时最小化肌肉刺激的平方和膝关节负荷。接下来，我们预测了肌肉无力如何导致正常行走运动的偏差。最后，我们预测了一个蹲下到站立的运动，并优化了放置在膝盖上的辅助装置的刚度。我们将Moco设计为易于使用，可定制和可扩展的，从而加速使用模拟来了解人类和其他动物的运动。计算机模拟已成为研究肌肉骨骼系统的一种日益流行的工具。模拟被用于研究肌肉在行走和跑步中的作用，分析神经系统疾病患者的步态，以及设计假肢和外骨骼。从历史上看，研究人员依靠实验数据来模拟和估计肌肉活动。基于直接配置最优控制方法的现代仿真方法使研究人员不仅可以估计肌肉活动，还可以在不需要实验数据的情况下预测新的运动。然而，直接配置方法实施起来困难且耗时，并且需要最优控制和优化理论方面的专业知识。在这里，我们介绍OpenSim Moco，这是一个开源软件包，可以预测没有最优控制背景的人可以访问的新动作。Moco利用OpenSim肌肉骨骼建模包提供的现有建模工具，并提供易于使用的界面，便于生成和共享仿真管道。Moco是模块化的，易于扩展，并包含一个测试套件，可以用已知的解决方案解决问题。我们提供的例子包括预测肌肉活动，使膝关节负荷最小化，预测肌肉无力如何影响正常行走，以及优化膝关节外骨骼以辅助下蹲站立运动。
Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves—which typically requires extensive technical expertise—and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals. Computer simulation has become an increasingly popular tool for studying the musculoskeletal system. Simulations are used to study the role of muscles in walking and running, to analyze the gait of individuals with neurological disease, and to design prostheses and exoskeletons. Historically, researchers have relied on experimental data to generate simulations and estimate muscle activity. Modern simulation approaches based on the direct collocation optimal control method allow researchers to not only estimate muscle activity, but also predict new motions without the need for experimental data. However, direct collocation methods are difficult and time-consuming to implement and require expertise in optimal control and optimization theory. Here we introduce OpenSim Moco, an open source software package that makes predicting new motions accessible to those without an optimal control background. Moco leverages the existing modeling tools offered by the OpenSim musculoskeletal modeling package and provides an easy-to-use interface that facilitates generating and sharing simulation pipelines. Moco is modular and easily extensible and includes a testing suite that solves problems with known solutions. We provide examples including predicting muscle activity that minimizes knee loading, predicting how muscle weakness affects normal walking, and optimizing a knee exoskeleton to assist a squat-to-stand motion.
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影响因子：
2.8
作者：
DeMers, Matthew S.;Pal, Saikat;Delp, Scott L.
通讯作者：
Delp, Scott L.
影响因子：
4.6
作者：
Falisse, Antoine;Van Rossom, Sam;De Groote, Friedl
通讯作者：
De Groote, Friedl
影响因子：
1.8
作者：
Bobbert, Maarten F.;Kistemaker, Dinant A.;Ackermann, Marko
通讯作者：
Ackermann, Marko
影响因子：
4.6
作者：
Fregly, Benjamin J.;Reinbolt, Jeffrey A.;Chmielewski, Terese L.
通讯作者：
Chmielewski, Terese L.
影响因子：
3.8
作者：
De Groote F;Kinney AL;Rao AV;Fregly BJ
通讯作者：
Fregly BJ