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.

Transtibial limb loss does not increase metabolic cost in three-dimensional computer simulations of human walking

“…The shoulder and elbow joints were actuated by ideal torque generators with activation dynamics. The model had a total body mass of 75.4 kg and height 1.75 m. Several changes or additions were made to the original model, which lacked some elements necessary or beneficial for optimal control simulations, to make it well suited for direct collocation simulations in Moco software (Dembia et al, 2020): Full-size DOI: 10.7717/peerj.11960/fig- 1 1. The original ''Millard2012Equilibrium''-type muscles were replaced with ''DeGrooteFregly2016''-type muscles, which have activation and contractile dynamics suitable for gradient-based optimization in Moco (De Groote et al, 2016).…”

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

“…The disadvantages of this approach are that repeated forward dynamic simulations are susceptible to numerical integration drift and other integration problems, especially when intermittent foot-ground contact is involved, plus unstable movements such as walking cannot be predicted without some form of stabilizing feedback control. To circumvent these problems, researchers have recently converged on direct collocation as the preferred method for predicting human movement [25,26,190,232,242,243,245,247,248]. For this method, the optimization design variables are model controls as well as states, repeated forward dynamic simulations are performed implicitly as part of the optimization problem formulation, and all time frames are solved simultaneously, thereby eliminating time marching.…”

“…While this approach produces a larger nonlinear optimization problem, it eliminates numerical integration drift and stability problems, and it is generally more reliable for predicting new motions. While some researchers have implemented their own direct collocation optimal control methods [190,191,242,243,245,247,[249][250][251], implementation of these methods is non-trivial, leading other researchers to use academic direct collocation software such as GPOPS-II [25,26], CasADi [135,136], and OpenSim Moco [248]. Despite the availability of these programs, incorporating a generic or personalized neuromusculoskeletal model into one of these packages remains challenging, limiting the use of this approach by the neuromusculoskeletal modeling research community.…”

A Review of Forward-Dynamics Simulation Models for Predicting Optimal Technique in Maximal Effort Sporting Movements

“…This function is then maximized or minimized by varying specified model inputs within realistic pre-determined limits using optimization algorithms such as simulated annealing [163] or genetic algorithm [164]. Alternative optimization approaches such as direct collocation optimal control [165][166][167] have been utilized more frequently in the muscle-driven model literature and may enhance computational speed. If attempting to predict optimum performance, the objective function must represent the task objective of the modeled activity.…”