OpenSim Moco: Musculoskeletal optimal control

Abstract

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.

Author summary

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.