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  • Title: Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models.
    Author: Clancy EA, Liu L, Liu P, Moyer DV.
    Journal: IEEE Trans Biomed Eng; 2012 Jan; 59(1):205-12. PubMed ID: 21968709.
    Abstract:
    The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 (-3) on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1.
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