An EMG frequency-based test for estimating the neuromuscular fatigue threshold during cycle ergometry View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-10-08

AUTHORS

Clayton L. Camic, Terry J. Housh, Glen O. Johnson, C. Russell Hendrix, Jorge M. Zuniga, Michelle Mielke, Richard J. Schmidt

ABSTRACT

The purposes of this investigation were twofold: (1) to determine if the model used for estimating the physical working capacity at the fatigue threshold (PWCFT) from electromyographic (EMG) amplitude data could be applied to the frequency domain of the signal to derive a new fatigue threshold for cycle ergometry called the mean power frequency fatigue threshold (MPFFT), and (2) to compare the power outputs associated with the PWCFT, MPFFT, ventilatory threshold (VT), and respiratory compensation point (RCP). Sixteen men [mean (SD) age = 23.4 (3.2) years] performed incremental cycle ergometer rides to exhaustion with bipolar surface EMG signals recorded from the vastus lateralis. There were significant (p < 0.05) mean differences for PWCFT [mean (SD) = 168 (36) W] versus MPFFT [208 (37) W] and VT [152 (33) W] versus RCP [205 (84) W], but no mean differences for PWCFT versus VT or MPFFT versus RCP. The mean difference between PWCFT and MPFFT may be due to the effects of specific metabolites that independently influence the time and frequency domains of the EMG signal. These findings indicated that the PWCFT model could be applied to the frequency domain of the EMG signal to estimate MPFFT. Furthermore, the current findings suggested that the PWCFT may demarcate the moderate from heavy exercise domains, while the MPFFT demarcates heavy from severe exercise intensities. More... »

PAGES

337

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00421-009-1239-7

DOI

http://dx.doi.org/10.1007/s00421-009-1239-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017880082

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19813019


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