Font-Llagunes, J. M. | Search bioRxiv

bioRxiv2026

A PRISMA-guided systematic review of musculoskeletal modelling approaches in lower-limb cycling biomechanics

Cycling is commonly employed in sports performance, rehabilitation, and clinical contexts, while musculoskeletal (MSK) simulations enable the investigation of internal biomechanics that cannot be measured experimentally. Despite growing use, the application, validation, and standardisation of MSK simulations in cycling remain unclear. This review aimed to systematically characterise the application, validation strategies, modelling assumptions, and reporting practices of musculoskeletal simulations in lower-limb cycling biomechanics. Searches were performed in Scopus, PubMed, IEEE Xplore, and Web of Science on 1 August 2024, covering studies from January 2010 to July 2024. Peer-reviewed English-language journal articles applying MSK simulations to lower-limb cycling were included; inverse kinematics-only was excluded. No protocol was registered, and no formal risk-of-bias assessment was conducted, as there were no intervention effects and no quantitative synthesis. Twenty-eight studies met the inclusion criteria. Most of them investigated bicycle-rider configuration, neuromuscular coordination, or electrical stimulation control, with participant cohorts overwhelmingly composed of young men and minimal female representation (272 total). Model reporting was often incomplete, with wide variation in anatomical scope, inconsistent descriptions of degrees of freedom, and limited sharing of models or code. Use of experimental data was uneven across studies: while all incorporated kinematic measurements, only two-thirds included kinetic data, and only one study reported physiological measures. Model validation was generally based on literature values. Seventy-eight per cent of studies used optimisation, mainly with effort-based cost functions, and parameter variations were exploratory rather than systematic. The evidence base is limited by small, predominantly male cohorts, inconsistent reporting standards, and limited physiological validation. These results consolidate current practices and highlight the need for more transparent and open reporting, sex-balanced and clinically diverse participant representation, stronger validation, and more rigorous sensitivity analysis to enhance reproducibility and practical relevance. This review was funded by AGAUR (Spain), CAPES (Brazil) and FAP-DF (Brazil). Author summaryCycling is widely used in sports training, rehabilitation, and clinical practice, and musculoskeletal simulations are increasingly used to study how muscles and joints work during cycling. These simulations allow us to estimate internal biomechanical variables that cannot be directly measured in experiments, such as muscle forces and joint loading. However, it is currently unclear how consistently these simulations are applied, validated, and reported across the literature. In this study, we systematically reviewed research published over the past 15 years that used musculoskeletal simulations to analyse lower-limb cycling. We identified 28 relevant studies and examined their modelling choices, experimental inputs, optimisation strategies, and validation approaches. We found substantial variability in model complexity, limited transparency in reporting, and a strong reliance on simplified literature-based validation methods. Most studies focused on narrow participant groups and explored modelling parameters in an ad hoc rather than a systematic way. Our findings highlight important gaps in current practice and point to clear opportunities for improvement. We provide an overview of common approaches and their limitations, and outline key recommendations to enhance the transparency, reproducibility, and practical relevance of musculoskeletal simulations in cycling research.

neuroscience↗

bioRxiv2025

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

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.

bioengineering↗