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dc.contributor.authorMichaud, Florian
dc.contributor.authorFrey-Law, Laura A.
dc.contributor.authorLugrís-Armesto, Urbano
dc.contributor.authorCuadrado, Lucía
dc.contributor.authorFigueroa-Rodríguez, Jesús
dc.contributor.authorCuadrado, Javier
dc.date.accessioned2023-10-06T06:53:39Z
dc.date.available2023-10-06T06:53:39Z
dc.date.issued2023-04
dc.identifier.citationMichaud F, Frey-Law LA, Lugrís U, Cuadrado L, Figueroa-Rodríguez J and Cuadrado J (2023), Applying a muscle fatigue model when optimizing loadsharing between muscles for shortduration high-intensity exercise: A preliminary study. Front. Physiol. 14:1167748. doi: 10.3389/fphys.2023.1167748es_ES
dc.identifier.issn1664-042X
dc.identifier.urihttp://hdl.handle.net/2183/33543
dc.description.abstract[Abstract] 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.es_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación; PGC 2018-095145-B-I00es_ES
dc.description.sponsorshipXunta de Galicia; ED431C2019/29es_ES
dc.description.sponsorshipXunta de Galicia; 2022/CP/048es_ES
dc.language.isoenges_ES
dc.publisherFrontierses_ES
dc.relation.urihttps://doi.org/10.3389/fphys.2023.1167748es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectMuscle forcees_ES
dc.subjectMultibody dynamicses_ES
dc.subjectInjury preventiones_ES
dc.subjectSport performancees_ES
dc.subjectMuscle fatigue modeles_ES
dc.subjectMusculotendon modeles_ES
dc.subjectMusculotendon dynamices_ES
dc.subjectErgonomicses_ES
dc.titleApplying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleFrontiers in Physiologyes_ES
UDC.volume14es_ES
UDC.startPage1es_ES
UDC.endPage16es_ES
dc.identifier.doihttps://doi.org/10.3389/fphys.2023.1167748


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