Document Detail

Linear vs. non-linear mapping of peak power using surface EMG features during dynamic fatiguing contractions.
MedLine Citation:
PMID:  20553798     Owner:  NLM     Status:  MEDLINE    
This study compares a non-linear (neural network) and a linear (linear regression) power mapping using a set of features of the surface electromyogram recorded from the vastus medialis and lateralis muscles. Fifteen healthy participants performed 5 sets of 10 repetitions leg press using the individual maximum load corresponding what they could perform 10 times (10RM) with 120s of rest between them. The following sEMG variables were computed from each extension contraction and used as inputs to both approaches: mean average value (MAV), median frequency (Fmed), the spectral parameter proposed by Dimitrov (FInsm5), average (over the observation interval) of the instantaneous mean frequency obtained from a Choi-Williams distribution (MFM), and wavelet indices ratio between moments at different scales (WIRM1551, WIRM1M51, WIRM1522, WIRE51, and WIRW51). The non-linear mapping (neural network) provided higher correlation coefficients and signal-to-noise ratios values (although not significantly different) between the actual and the estimated changes of power compared to linear mapping (linear regression) using the sEMG variables alone and a combination of WIRW51 and MFM (obtained by a stepwise multiple linear regression). In conclusion, non-linear mapping of force loss during dynamic knee extension exercise showed higher signal-to-noise ratio and correlation coefficients between the actual and estimated power output compared to linear mapping. However, since no significant differences were observed between linear and non-linear approaches, both were equally valid to estimate changes in peak power during fatiguing repetitive leg extension exercise.
M Gonzalez-Izal; A Malanda; I Rodríguez-Carreño; I Navarro-Amézqueta; E M Gorostiaga; D Farina; D Falla; M Izquierdo
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of biomechanics     Volume:  43     ISSN:  1873-2380     ISO Abbreviation:  J Biomech     Publication Date:  2010 Sep 
Date Detail:
Created Date:  2010-09-10     Completed Date:  2011-01-06     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0157375     Medline TA:  J Biomech     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2589-94     Citation Subset:  IM    
Department of Electric and Electronic Engineering, Public University of Navarre, Campus de Arrosadia, Pamplona, Spain.
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MeSH Terms
Electromyography / methods*
Knee / physiology
Muscle Contraction / physiology*
Muscle Fatigue / physiology
Neural Networks (Computer)*
Quadriceps Muscle / physiology*

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