Four-compartment muscle fatigue model to predict metabolic inhibition and long-lasting nonmetabolic components

Simple item page
UDC.coleccionInvestigaciónes_ES
UDC.departamentoEnxeñaría Naval e Industriales_ES
UDC.grupoInvLaboratorio de Enxeñaría Mecánica (LIM)es_ES
UDC.journalTitleFrontiers in Physiologyes_ES
UDC.volume15es_ES
dc.contributor.authorMichaud, Florian
dc.contributor.authorBeron, Santiago
dc.contributor.authorLugrís-Armesto, Urbano
dc.contributor.authorCuadrado, Javier
dc.date.accessioned2024-07-29T08:51:35Z
dc.date.available2024-07-29T08:51:35Z
dc.date.issued2024-03-11
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.sponsorshipinfo: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 MUSCULARes_ES
dc.description.sponsorshipGrant 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.sponsorshipXunta de Galicia; ED431C 2023/01es_ES
dc.description.sponsorshipXunta de Galicia; 2022/CP/048es_ES
dc.identifier.citationMichaud 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.2024.1366172es_ES
dc.identifier.doihttps://doi.org/10.3389/fphys.2024.1366172
dc.identifier.issn1664-042X
dc.identifier.urihttp://hdl.handle.net/2183/38283
dc.language.isoenges_ES
dc.publisherLausanne Frontiers Media S.A.es_ES
dc.relation.urihttps://doi.org/10.3389/fphys.2024.1366172es_ES
dc.rightsCreative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/*
dc.subjectMusculotendon dynamicses_ES
dc.subjectMuscle forcees_ES
dc.subjectMuscle fatigue modeles_ES
dc.subjectForce predictiones_ES
dc.subjectMusculotendon modelses_ES
dc.subjectSport performancees_ES
dc.subjectErgonomicses_ES
dc.subjectMathematical modelses_ES
dc.titleFour-compartment muscle fatigue model to predict metabolic inhibition and long-lasting nonmetabolic componentses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication9ae321fb-7c78-4cb9-a919-c21ae7ee1ab0
relation.isAuthorOfPublication4197d7d5-07cc-4882-b80c-5235c528fc64
relation.isAuthorOfPublication27a382bf-5c39-425c-84ea-43eace4efdff
relation.isAuthorOfPublication2e638887-48a4-49dd-9a45-6060a48d7aab
relation.isAuthorOfPublication.latestForDiscovery9ae321fb-7c78-4cb9-a919-c21ae7ee1ab0

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Michaud_Florian_2024_Four_compartment_muscle_fatigue_model.pdf
Size:
2.26 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Investigación (EPEF)