Literature DB >> 11007565

A predictive model of fatigue in human skeletal muscles.

J Ding1, A S Wexler, S A Binder-Macleod.   

Abstract

Fatigue is a major limitation to the clinical application of functional electrical stimulation. The activation pattern used during electrical stimulation affects force and fatigue. Identifying the activation pattern that produces the greatest force and least fatigue for each patient is, therefore, of great importance. Mathematical models that predict muscle forces and fatigue produced by a wide range of stimulation patterns would facilitate the search for optimal patterns. Previously, we developed a mathematical isometric force model that successfully identified the stimulation patterns that produced the greatest forces from healthy subjects under nonfatigue and fatigue conditions. The present study introduces a four-parameter fatigue model, coupled with the force model that predicts the fatigue induced by different stimulation patterns on different days during isometric contractions. This fatigue model accounted for 90% of the variability in forces produced by different fatigue tests. The predicted forces at the end of fatigue testing differed from those observed by only 9%. This model demonstrates the potential for predicting muscle fatigue in response to a wide range of stimulation patterns.

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Mesh:

Year:  2000        PMID: 11007565     DOI: 10.1152/jappl.2000.89.4.1322

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  15 in total

1.  Predicting human chronically paralyzed muscle force: a comparison of three mathematical models.

Authors:  Laura A Frey Law; Richard K Shields
Journal:  J Appl Physiol (1985)       Date:  2005-11-23

2.  Mathematical model that predicts the force-intensity and force-frequency relationships after spinal cord injuries.

Authors:  Jun Ding; Li-Wei Chou; Trisha M Kesar; Samuel C K Lee; Therese E Johnston; Anthony S Wexler; Stuart A Binder-Macleod
Journal:  Muscle Nerve       Date:  2007-08       Impact factor: 3.217

3.  Dynamic optimization of stimulation frequency to reduce isometric muscle fatigue using a modified Hill-Huxley model.

Authors:  Brian D Doll; Nicholas A Kirsch; Xuefeng Bao; Brad E Dicianno; Nitin Sharma
Journal:  Muscle Nerve       Date:  2017-09-18       Impact factor: 3.217

4.  Effects of stimulation frequency versus pulse duration modulation on muscle fatigue.

Authors:  Trisha Kesar; Li-Wei Chou; Stuart A Binder-Macleod
Journal:  J Electromyogr Kinesiol       Date:  2007-02-21       Impact factor: 2.368

5.  In vivo demonstration of a self-sustaining, implantable, stimulated-muscle-powered piezoelectric generator prototype.

Authors:  B E Lewandowski; K L Kilgore; K J Gustafson
Journal:  Ann Biomed Eng       Date:  2009-08-06       Impact factor: 3.934

6.  Fatigue and non-fatigue mathematical muscle models during functional electrical stimulation of paralyzed muscle.

Authors:  Zhijun Cai; Er-Wei Bai; Richard K Shields
Journal:  Biomed Signal Process Control       Date:  2010-04       Impact factor: 3.880

7.  Mechanisms of in vivo muscle fatigue in humans: investigating age-related fatigue resistance with a computational model.

Authors:  Damien M Callahan; Brian R Umberger; Jane A Kent
Journal:  J Physiol       Date:  2016-03-02       Impact factor: 5.182

8.  A three-compartment muscle fatigue model accurately predicts joint-specific maximum endurance times for sustained isometric tasks.

Authors:  Laura A Frey-Law; John M Looft; Jesse Heitsman
Journal:  J Biomech       Date:  2012-05-09       Impact factor: 2.712

9.  Development of a mathematical model for predicting electrically elicited quadriceps femoris muscle forces during isovelocity knee joint motion.

Authors:  Ramu Perumal; Anthony S Wexler; Stuart A Binder-Macleod
Journal:  J Neuroeng Rehabil       Date:  2008-12-10       Impact factor: 4.262

10.  Predicting non-isometric fatigue induced by electrical stimulation pulse trains as a function of pulse duration.

Authors:  M Susan Marion; Anthony S Wexler; Maury L Hull
Journal:  J Neuroeng Rehabil       Date:  2013-02-02       Impact factor: 4.262

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