A predictive fatigue model - II: Predicting the effect of resting times on fatigue

Jun Ding, Anthony S. Wexler, Stuart A. Binder-Macleod

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

We have recently developed a force- and fatigue-model system that accurately predicted the effect of stimulation frequency on muscle fatigue. The data used to test the model were produced by stimulation trains with resting times of 500 ms. Because the resting times between stimulation trains affect muscle fatigue, this study tested the model's ability to predict the effect of resting times on fatigue. In addition, because this study included different subjects than those used to develop the model, the validity of the model could be tested. Data were collected from human quadriceps femoris muscles using fatigue protocols that included resting times of 500, 750, or 1000 ms. Our results showed that the model predicted fatigue as being a decreasing function of resting time, which was consistent with experimental data. Reliability tests between the experimental data and predictions showed interclass correlation coefficients of 0.97, 0.95, and 0.81 for the initial, final, and percentage decline in peak forces, respectively, suggesting strong agreement between the experimental data and the predictions by the model. The success of our current force- and fatigue-model system helps to validate the model and suggests its potential use in identifying the optimal activation pattern during clinical application of functional electrical stimulation.

Original languageEnglish (US)
Pages (from-to)59-67
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume10
Issue number1
DOIs
StatePublished - 2002
Externally publishedYes

    Fingerprint

Keywords

  • Functional electrical stimulation
  • Muscle fatigues model
  • Muscle force model

ASJC Scopus subject areas

  • Rehabilitation
  • Biophysics
  • Bioengineering
  • Health Professions(all)

Cite this