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  4. Investigation of the dependence of joint contact forces on musculotendon parameters using a codified workflow for image-based modelling
 
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2018
Journal Article
Title

Investigation of the dependence of joint contact forces on musculotendon parameters using a codified workflow for image-based modelling

Abstract
The generation of subject-specific musculoskeletal models of the lower limb has become a feasible task thanks to improvements in medical imaging technology and musculoskeletal modelling software. Nevertheless, clinical use of these models in paediatric applications is still limited for what concerns the estimation of muscle and joint contact forces. Aiming to improve the current state of the art, a methodology to generate highly personalized subject-specific musculoskeletal models of the lower limb based on magnetic resonance imaging (MRI) scans was codified as a step-by-step procedure and applied to data from eight juvenile individuals. The generated musculoskeletal models were used to simulate 107 gait trials using stereophotogrammetric and force platform data as input. To ensure completeness of the modelling procedure, muscles' architecture needs to be estimated. Four methods to estimate muscles' maximum isometric force and two methods to estimate musculotendon parameters (optimal fiber length and tendon slack length) were assessed and compared, in order to quantify their influence on the models' output. Reported results represent the first comprehensive subject-specific model-based characterization of juvenile gait biomechanics, including profiles of joint kinematics and kinetics, muscle forces and joint contact forces. Our findings suggest that, when musculotendon parameters were linearly scaled from a reference model and the muscle force-length-velocity relationship was accounted for in the simulations, realistic knee contact forces could be estimated and these forces were not sensitive the method used to compute muscle maximum isometric force.
Author(s)
Modenese, Luca
Univ. of Sheffield
Montefiori, Erica
Univ. of Sheffield
Wang, Anqi
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Wesarg, Stefan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Viceconti, Marco
Univ. of Sheffield
Mazzà, Claudia
Univ. of Sheffield
Journal
Journal of biomechanics  
Project(s)
MD PAEDIGREE  
Funder
European Commission EC  
Open Access
DOI
10.1016/j.jbiomech.2018.03.039
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • biomechanics

  • Lead Topic: Individual Health

  • Research Line: Computer vision (CV)

  • Research Line: Modeling (MOD)

  • medical imaging

  • modeling

  • biomedical computing

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