Neuromusculoskeletal injuries including osteoarthritis, stroke, spinal cord injury, and traumatic brain injury affect roughly 19% of the U.S. adult population. Standardized interventions have produced suboptimal functional outcomes due to the unique treatment needs of each patient. Strides have been made to utilize computational models to develop personalized treatments, but researchers and clinicians have yet to cross the “valley of death” between fundamental research and clinical usefulness. This article introduces the Neuromusculoskeletal Modeling (NMSM) Pipeline, two MATLAB-based toolsets that add Model Personalization and Treatment Optimization functionality to OpenSim. The two toolsets facilitate computational design of individualized treatments for neuromusculoskeletal impairments through the use of personalized neuromusculoskeletal models and predictive simulation. The Model Personalization toolset contains four tools for personalizing 1) joint structure models, 2) muscle-tendon models, 3) neural control models, and 4) foot-ground contact models. The Treatment Optimization toolset contains three tools for predicting and optimizing a patient’s functional outcome for different treatment options using a patient’s personalized neuromusculoskeletal model with direct collocation optimal control methods.

Two NMSM Pipeline use cases are presented. The first example is an individual post-stroke with impaired walking function, where the goal is to predict how the subject’s neural control could be changed to improve walking speed without increasing metabolic cost. First the Model Personalization toolset was used to develop a personalized neuromusculoskeletal model of the subject starting from a generic OpenSim full-body model and experimental walking data (video motion capture, ground reaction, and electromyography) collected from the subject at his self-selected speed. Next the Treatment Optimization toolset was used with the personalized model to predict how the subject could recruit existing muscle synergies more effectively to reduce muscle activation disparities between the paretic and non-paretic legs. The software predicted that the subject could increase his walking speed by 60% without increasing his metabolic cost per unit time by modifying the recruitment of his existing muscle synergies. This hypothetical treatment demonstrates how NMSM Pipeline tools could allow researchers working collaboratively with clinicians to develop personalized neuromusculoskeletal models of individual patients and to perform predictive simulations for the purpose of designing personalized treatments that maximize a patient’s post-treatment functional outcome.

The second example is a novel personalized closed-chain kinematic shoulder model creation process utilizing the first Model Personalization tool, Joint Model Personalization. Commonly used kinematic shoulder models typically use regression-based kinematics and open-chain constructions, these models can produce low accuracy and anatomically impossible kinematics for many motions and subjects. After creating synthetic marker data and a model compatible with the NMSM Pipeline, joint parameters were automatically optimized to minimize the error between modeled kinematics and experimental kinematics of eight motions. The software produced a series of models with average marker distance errors below 1 millimeter across all motions for the best 5 degree of freedom model. This novel personalized closed-chain kinematic shoulder illustrates the ability of the NMSM Pipeline to influence the field of neuromusculoskeletal modeling