Cargando…

Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study

Introduction: Multiple different mathematical models have been developed to represent muscle force, to represent multiple muscles in the musculoskeletal system, and to represent muscle fatigue. However, incorporating these different models together to describe the behavior of a high-intensity exerci...

Descripción completa

Detalles Bibliográficos
Autores principales: Michaud, Florian, Frey-Law, Laura A., Lugrís, Urbano, Cuadrado, Lucía, Figueroa-Rodríguez, Jesús, Cuadrado, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165736/
https://www.ncbi.nlm.nih.gov/pubmed/37168228
http://dx.doi.org/10.3389/fphys.2023.1167748
_version_ 1785038308727848960
author Michaud, Florian
Frey-Law, Laura A.
Lugrís, Urbano
Cuadrado, Lucía
Figueroa-Rodríguez, Jesús
Cuadrado, Javier
author_facet Michaud, Florian
Frey-Law, Laura A.
Lugrís, Urbano
Cuadrado, Lucía
Figueroa-Rodríguez, Jesús
Cuadrado, Javier
author_sort Michaud, Florian
collection PubMed
description Introduction: Multiple different mathematical models have been developed to represent muscle force, to represent multiple muscles in the musculoskeletal system, and to represent muscle fatigue. However, incorporating these different models together to describe the behavior of a high-intensity exercise has not been well described. Methods: In this work, we adapted the three-compartment controller (3CCr) muscle fatigue model to be implemented with an inverse-dynamics based optimization algorithm for the muscle recruitment problem for 7 elbow muscles to model a benchmark case: elbow flexion/extension moments. We highlight the difficulties in achieving an accurate subject-specific approach for this multi-level modeling problem, considering different muscular models, compared with experimental measurements. Both an isometric effort and a dynamic bicep curl were considered, where muscle activity and resting periods were simulated to obtain the fatigue behavior. Muscle parameter correction, scaling and calibration are addressed in this study. Moreover, fiber-type recruitment hierarchy in force generation was added to the optimization problem, thus offering an additional novel muscle modeling criterion. Results: It was observed that: i) the results were most accurate for the static case; ii) insufficient torque was predicted by the model at some time points for the dynamic case, which benefitted from a more precise calibration of muscle parameters; iii) modeling the effects of muscular potentiation may be important; and iv) for this multilevel model approach, the 3CCr model had to be modified to avoid reaching situations of unrealistic constant fatigue in high intensity exercise-resting cycles. Discussion: All the methods yield reasonable estimations, but the complexity of obtaining accurate subject-specific human models is highlighted in this study. The proposed novel muscle modeling and force recruitment criterion, which consider the muscular fiber-type distinction, show interesting preliminary results.
format Online
Article
Text
id pubmed-10165736
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-101657362023-05-09 Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study Michaud, Florian Frey-Law, Laura A. Lugrís, Urbano Cuadrado, Lucía Figueroa-Rodríguez, Jesús Cuadrado, Javier Front Physiol Physiology Introduction: Multiple different mathematical models have been developed to represent muscle force, to represent multiple muscles in the musculoskeletal system, and to represent muscle fatigue. However, incorporating these different models together to describe the behavior of a high-intensity exercise has not been well described. Methods: In this work, we adapted the three-compartment controller (3CCr) muscle fatigue model to be implemented with an inverse-dynamics based optimization algorithm for the muscle recruitment problem for 7 elbow muscles to model a benchmark case: elbow flexion/extension moments. We highlight the difficulties in achieving an accurate subject-specific approach for this multi-level modeling problem, considering different muscular models, compared with experimental measurements. Both an isometric effort and a dynamic bicep curl were considered, where muscle activity and resting periods were simulated to obtain the fatigue behavior. Muscle parameter correction, scaling and calibration are addressed in this study. Moreover, fiber-type recruitment hierarchy in force generation was added to the optimization problem, thus offering an additional novel muscle modeling criterion. Results: It was observed that: i) the results were most accurate for the static case; ii) insufficient torque was predicted by the model at some time points for the dynamic case, which benefitted from a more precise calibration of muscle parameters; iii) modeling the effects of muscular potentiation may be important; and iv) for this multilevel model approach, the 3CCr model had to be modified to avoid reaching situations of unrealistic constant fatigue in high intensity exercise-resting cycles. Discussion: All the methods yield reasonable estimations, but the complexity of obtaining accurate subject-specific human models is highlighted in this study. The proposed novel muscle modeling and force recruitment criterion, which consider the muscular fiber-type distinction, show interesting preliminary results. Frontiers Media S.A. 2023-04-24 /pmc/articles/PMC10165736/ /pubmed/37168228 http://dx.doi.org/10.3389/fphys.2023.1167748 Text en Copyright © 2023 Michaud, Frey-Law, Lugrís, Cuadrado, Figueroa-Rodríguez and Cuadrado. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Michaud, Florian
Frey-Law, Laura A.
Lugrís, Urbano
Cuadrado, Lucía
Figueroa-Rodríguez, Jesús
Cuadrado, Javier
Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study
title Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study
title_full Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study
title_fullStr Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study
title_full_unstemmed Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study
title_short Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study
title_sort applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: a preliminary study
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165736/
https://www.ncbi.nlm.nih.gov/pubmed/37168228
http://dx.doi.org/10.3389/fphys.2023.1167748
work_keys_str_mv AT michaudflorian applyingamusclefatiguemodelwhenoptimizingloadsharingbetweenmusclesforshortdurationhighintensityexerciseapreliminarystudy
AT freylawlauraa applyingamusclefatiguemodelwhenoptimizingloadsharingbetweenmusclesforshortdurationhighintensityexerciseapreliminarystudy
AT lugrisurbano applyingamusclefatiguemodelwhenoptimizingloadsharingbetweenmusclesforshortdurationhighintensityexerciseapreliminarystudy
AT cuadradolucia applyingamusclefatiguemodelwhenoptimizingloadsharingbetweenmusclesforshortdurationhighintensityexerciseapreliminarystudy
AT figueroarodriguezjesus applyingamusclefatiguemodelwhenoptimizingloadsharingbetweenmusclesforshortdurationhighintensityexerciseapreliminarystudy
AT cuadradojavier applyingamusclefatiguemodelwhenoptimizingloadsharingbetweenmusclesforshortdurationhighintensityexerciseapreliminarystudy