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Four-compartment muscle fatigue model to predict metabolic inhibition and long-lasting nonmetabolic components
dc.contributor.author | Michaud, Florian | |
dc.contributor.author | Beron, Santiago | |
dc.contributor.author | Lugrís-Armesto, Urbano | |
dc.contributor.author | Cuadrado, Javier | |
dc.date.accessioned | 2024-07-29T08:51:35Z | |
dc.date.available | 2024-07-29T08:51:35Z | |
dc.date.issued | 2024-03-11 | |
dc.identifier.citation | Michaud F, Beron S, Lugrís U and Cuadrado J (2024), Four-compartment muscle fatigue model to predict metabolic inhibition and longlasting nonmetabolic components. Front. Physiol. 15:1366172. doi: 10.3389/fphys.2024.1366172 | es_ES |
dc.identifier.issn | 1664-042X | |
dc.identifier.uri | http://hdl.handle.net/2183/38283 | |
dc.description.abstract | [Abstract] Computational muscle force models aim to mathematically represent the mechanics of movement and the factors influencing force generation. These tools allow the prediction of the nonlinear and task-related muscle behavior, aiding biomechanics, sports science, and rehabilitation. Despite often overlooking muscle fatigue in low-force scenarios, these simulations are crucial for high-intensity activities where fatigue and force loss play a significant role. Applications include functional electrical stimulation, motor control, and ergonomic considerations in diverse contexts, encompassing rehabilitation and the prevention of injuries in sports and workplaces. | es_ES |
dc.description.sponsorship | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-140062OB-I00/ES/CAPTURA, RECONSTRUCCION Y ANALISIS NEURO-MUSCULO-ESQUELETICO DEL MOVIMIENTO HUMANO EN TIEMPO REAL, CON CONSIDERACION DE LA FATIGA MUSCULAR | es_ES |
dc.description.sponsorship | Grant ED431C 2023/01 by the Galician Government. Moreover, FM would like to acknowledge the support of the Galician Government and the Ferrol Industrial Campus by means of the postdoctoral research contract 2022/CP/048. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2023/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; 2022/CP/048 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Lausanne Frontiers Media S.A. | es_ES |
dc.relation.uri | https://doi.org/10.3389/fphys.2024.1366172 | es_ES |
dc.rights | Creative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Musculotendon dynamics | es_ES |
dc.subject | Muscle force | es_ES |
dc.subject | Muscle fatigue model | es_ES |
dc.subject | Force prediction | es_ES |
dc.subject | Musculotendon model | es_ES |
dc.subject | Sport performance | es_ES |
dc.subject | Ergonomics | es_ES |
dc.subject | Mathematical models | es_ES |
dc.title | Four-compartment muscle fatigue model to predict metabolic inhibition and long-lasting nonmetabolic components | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Frontiers in Physiology | es_ES |
UDC.volume | 15 | es_ES |
dc.identifier.doi | https://doi.org/10.3389/fphys.2024.1366172 |
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