Validation of a Musculoskeletal Model for Simulating Muscle Mechanics and Energetics During Diverse Human Hopping Tasks

Abstract

Abstract Computational musculoskeletal modelling has emerged as an alternative technique to indirect calorimetry for estimating energy expenditure. In comparison, musculoskeletal modelling is less constrained – removing the need for repetitive motion, often at steady-state, over prolonged time periods. However, predictions from modelling tools depend on many assumptions around muscle architecture and function and motor control. Therefore, these tools need to continue to be validated if we are to eventually develop subject-specific simulations that can accurately and reliably model rates of energy consumption for any given task. In this study, we used OpenSim software and experimental motion capture data to simulate muscle activations, muscle fascicle dynamics, and whole-body metabolic power across mechanically and energetically disparate hopping tasks, and then evaluated these outputs at a group- and individual-level against experimental electromyography, ultrasound, and indirect colorimetry data. We found weak to strong correlations for peak muscle activations, moderate to strong correlations for absolute fascicle shortening and mean shortening velocity, and strong correlations for gross metabolic power. These correlations tended to be stronger on a group-level rather than individual-level. Therefore, current modelling approaches may be sufficient for predicting relative differences in metabolic power across movement conditions on a group-level, but caution is required for interpretation of simulation outputs for individuals. We also urge that appropriate validation be performed before running analyses of simulated muscle mechanics, especially of muscle activations. We encourage others to use our publicly available dataset and develop others alike to refine the modelling of muscle mechanics and, more globally, the subject-specificity of simulations.