OpenSim Moco: Musculoskeletal optimal control

• Published in: PLoS computational biology Vol. 16; no. 12; p. e1008493
• Main Authors: Dembia, Christopher L., Bianco, Nicholas A., Falisse, Antoine, Hicks, Jennifer L., Delp, Scott L.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.12.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Biomechanical Phenomena; Biomechanics; Computational biology; Computer simulation; Computer-generated environments; Control; Differential equations; Energy consumption; Humans; Kinematics; Libraries; Mechanical properties; Medicine and Health Sciences; Methods; Models, Biological; Movement - physiology; Muscle function; Muscles; Musculoskeletal Physiological Phenomena; Musculoskeletal system; Optimal control; Optimization; Parameters; Physical Sciences; Research and Analysis Methods; Researchers; Simulation; Software; Solvers; Stiffness; System dynamics; Walking; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008493

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.