title: Linear vs. non-linear mapping of peak power using surface EMG features during dynamic fatiguing contractions.
abstract: 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.
authors.affiliation: Department of Electric and Electronic Engineering, Public University of Navarre, Campus de Arrosadia, Pamplona, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Gonzalez-Izal
authors.forename: A
authors.initials: A
authors.lastname: Malanda
authors.forename: I
authors.initials: I
authors.lastname: Rodríguez-Carreño
authors.forename: I
authors.initials: I
authors.lastname: Navarro-Amézqueta
authors.forename: E M
authors.initials: EM
authors.lastname: Gorostiaga
authors.forename: D
authors.initials: D
authors.lastname: Farina
authors.forename: D
authors.initials: D
authors.lastname: Falla
authors.forename: M
authors.initials: M
authors.lastname: Izquierdo
mesh_terms: D000328:Adult; D004576:Electromyography / Q000379:methods*; D015444:Exercise; D006801:Humans; D007717:Knee / Q000502:physiology; D008297:Male; D009119:Muscle Contraction / Q000502:physiology*; D018763:Muscle Fatigue / Q000502:physiology; D016571:Neural Networks, Computer; D052097:Quadriceps Muscle / Q000502:physiology*; D016474:Weight-Bearing