sEMG Wavelet-based Indices predicts Muscle Power Loss during Dynamic Contractions (abstract only)
Miriam GonzÃ¡lez Izal () and
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Miriam GonzÃ¡lez Izal: Departamento de IngenierÃa ElÃ©ctrica y ElectrÃ³nica, Universidad PÃºblica de Navarra
No 17/09, Faculty Working Papers from School of Economics and Business Administration, University of Navarra
Purpose: To compare the sensitivity to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol from new surface electromyography (sEMG) indices based on the discrete wavelet transform, as well as from amplitude and spectral indices of muscle fatigue (i.e. mean average voltage, median frequency and ratios between spectral moments). Methods: 15 trained subjects performed 5 sets consisting of 10 leg press, with 2 minutes rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean average voltage (MAV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as other five parameters obtained from the discrete wavelet transform (DWT) as ratios between different scales. Results: The new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log FInsm5 and MAV as a two factor combination predictor accounted for 49.8%. On the other hand, they showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). Conclusions: The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.
Keywords: Median Frequency; surface EMG; wavelet transform; muscle fatigue. (search for similar items in EconPapers)
Pages: 2 pages
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Persistent link: https://EconPapers.repec.org/RePEc:una:unccee:wp1709
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