Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Abstract

Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.

Muscle-tendon mechanics explain unexpected effects of exoskeleton assistance on metabolic rate during walking

“…The average RMSE between musclegenerated and inverse dynamics-derived ankle joint powers was 0.19 W kg -1 , which was 9% of the average peak of the inverse dynamics-derived ankle joint power (2.2 W kg -1 ). Musclegenerated ankle joint moments were found to be within two standard deviations of inverse dynamics-derived ankle joint moments, on average, which has been considered acceptable by other researchers (Hicks et al, 2015). The error in the timing of peak subject-averaged joint moments and powers had a maximum value of 1.6% of the gait cycle across all conditions.…”

“…Other studies have reported that the combination of the soleus, medial gastrocnemius and lateral gastrocnemius contribute about 90% of the total ankle plantarflexion moment, and the tibialis anterior contributes more than 50% of the total ankle dorsiflexion moment in the model (Arnold et al, 2013), suggesting that these muscles are sufficient for generating realistic ankle joint mechanics. We did not, however, expect a perfect match between the two methods (Hicks et al, 2015). To obtain the optimal values of the scaling and delay factors for the muscles acting about the ankle joint, we performed gradient descent optimization.…”

The Influence of Component Alignment and Ligament Properties on Tibiofemoral Contact Forces in Total Knee Replacement

“…Additionally, musculoskeletal models of the type used in this study can contain thousands of parameters, which makes them subject to redundancy, where multiple parameter combinations could result in agreement with the experimental measures. Accordingly, it is important to test the robustness of model predictions to a range of reasonable parameter values, particularly for parameters with large influence or variability [78]. In addition to complementing model validation, sensitivity studies are useful to reveal causal relationships between model parameters and simulated outcomes, making them highly relevant to surgical simulation.…”

Musculoskeletal Modeling of the Hand and Contact Object in Modelica

“…The joints have restrictions on the total angle of rotation, and muscle actuation is added to the fingers appropriately. (Hicks et al, 2015) observes that mathematical modelers have a dual responsibility of verifying and validating both the physical equations in the model, and the mathematical solving components of the model. We aim to significantly reduce this challenge by keeping the physics of the system well-removed from the mathematics required to solve the models.…”