000063058 001__ 63058
000063058 005__ 20200729203741.0
000063058 0247_ $$2doi$$a10.1007/s10237-016-0841-y
000063058 0248_ $$2sideral$$a96684
000063058 037__ $$aART-2017-96684
000063058 041__ $$aeng
000063058 100__ $$0(orcid)0000-0002-6134-8698$$aSierra, M.$$uUniversidad de Zaragoza
000063058 245__ $$aPredicting muscle fatigue: a response surface approximation based on proper generalized decomposition technique
000063058 260__ $$c2017
000063058 5060_ $$aAccess copy available to the general public$$fUnrestricted
000063058 5203_ $$aA novel technique is proposed to predict force reduction in skeletal muscle due to fatigue under the influence of electrical stimulus parameters and muscle physiological characteristics. Twelve New Zealand white rabbits were divided in four groups ((Formula presented.)) to obtain the active force evolution of in vitro Extensor Digitorum Longus muscles for an hour of repeated contractions under different electrical stimulation patterns. Left and right muscles were tested, and a total of 24 samples were used to construct a response surface based in the proper generalized decomposition. After the response surface development, one additional rabbit was used to check the predictive potential of the technique. This multidimensional surface takes into account not only the decay of the maximum repeated peak force, but also the shape evolution of each contraction, muscle weight, electrical input signal and stimulation protocol. This new approach of the fatigue simulation challenge allows to predict, inside the multispace surface generated, the muscle response considering other stimulation patterns, different tissue weight, etc.
000063058 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T88$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2014-54981-R$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2013-TEC-09
000063058 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000063058 590__ $$a3.212$$b2017
000063058 591__ $$aENGINEERING, BIOMEDICAL$$b20 / 78 = 0.256$$c2017$$dQ2$$eT1
000063058 591__ $$aBIOPHYSICS$$b22 / 72 = 0.306$$c2017$$dQ2$$eT1
000063058 592__ $$a1.138$$b2017
000063058 593__ $$aBiotechnology$$c2017$$dQ1
000063058 593__ $$aModeling and Simulation$$c2017$$dQ1
000063058 593__ $$aMechanical Engineering$$c2017$$dQ1
000063058 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000063058 700__ $$0(orcid)0000-0002-6870-0594$$aGrasa, J.$$uUniversidad de Zaragoza
000063058 700__ $$0(orcid)0000-0001-8301-6902$$aMuñoz, M. J.$$uUniversidad de Zaragoza
000063058 700__ $$0(orcid)0000-0001-5981-5448$$aMiana-Mena, F.$$uUniversidad de Zaragoza
000063058 700__ $$0(orcid)0000-0003-3003-5856$$aGonzález, D.$$uUniversidad de Zaragoza
000063058 7102_ $$11005$$2410$$aUniversidad de Zaragoza$$bDpto. Farmacología y Fisiolog.$$cÁrea Fisiología
000063058 7102_ $$11005$$2315$$aUniversidad de Zaragoza$$bDpto. Farmacología y Fisiolog.$$cÁrea Farmacología
000063058 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000063058 773__ $$g16,  625-634 (2017), 1-10$$pBiomech. model. mechanobiol.$$tBIOMECHANICS AND MODELING IN MECHANOBIOLOGY$$x1617-7959
000063058 8564_ $$s1292883$$uhttps://zaguan.unizar.es/record/63058/files/texto_completo.pdf$$yPostprint
000063058 8564_ $$s98539$$uhttps://zaguan.unizar.es/record/63058/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000063058 909CO $$ooai:zaguan.unizar.es:63058$$particulos$$pdriver
000063058 951__ $$a2020-07-29-20:19:56
000063058 980__ $$aARTICLE