Can predictive simulations of walker-assisted gait using calibrated muscle models capture subject-specific walking features in individuals with spinal cord injury?

Abstract

ObjectiveThis study aims to evaluate whether our predictive simulation framework, coupled with different musculoskeletal model personalization methods, can reproduce the distinct subject-specific gait features of four subjects with spinal cord injury (SCI). MethodsMotion capture data was collected with four SCI patients. The musculotendon parameters of the musculoskeletal models of each subject were calibrated using three different methods: one anthropometric and two functional approaches. Predictive simulations of walker-assisted gait were performed using direct collocation in an optimal control problem. The cost function included terms minimizing metabolic energy rate and muscle effort, along with additional terms reflecting the instructions of the clinicians. Post-simulation analyses were carried out to compute key gait metrics and perform inter-subject and intra-subject comparisons in both the experimental data and gait predictive simulations. ResultsThe predictive simulations with functionally-calibrated models reproduced some distinct gait metrics for the four subjects with SCI. However, the predicted inter-subject variability of the kinematics (e.g., 7.81+/-6.04 deg for lower body joints) was generally statistically lower than the experimental one (e.g., 11.54+/-6.96 deg for lower body joints). In addition, when comparing subjects pairwise, in some cases, the predictive simulations were able to capture the similarities or discrepancies in kinematics and gait metrics between two individuals. Moreover, functionally-calibrated models yielded lower root mean square errors between the predicted and experimental lower body kinematics compared to models personalized with the anthropometric approach. ConclusionThe results suggest that our predictive simulation framework can reproduce some subject-specific gait features for patients with SCI. However, further work is required to improve the realism of the musculoskeletal models (e.g., by implementing a more detailed hand-walker contact model), enhance the formulation of the predictive simulations problem (e.g., by estimating the optimal weights of the control objectives using multi-objective optimization), and include more subjects for achieving more generalizable results. Author summaryAmong individuals with spinal cord injury, restoring gait is a primary rehabilitation goal to improve quality of life and decrease the risk of secondary health conditions. It is fundamental to choose and tailor a specific treatment to maximize the recovery of a specific patient. Predictive simulations of gait represent a promising approach for informing these clinical decision-making processes. They would allow us to evaluate multiple "what-if" scenarios prior to a treatment and help identify the intervention with the most favorable outcome. This work serves as a building block towards a potential use of predictive simulations in clinical applications. In fact, we assess whether such simulations can reproduce and distinguish the subject-specific gait patterns of individuals with spinal cord injury. Our findings suggest that we are able to predict some key gait metrics of specific patients. However, further work is needed to improve the realism of the computational models used in the predictive simulations before such approaches can be reliably applied in clinical settings.