title: Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA 01609, USA. ted@wpi.edu
authors.forename: Edward A
authors.initials: EA
authors.lastname: Clancy
authors.forename: Lukai
authors.initials: L
authors.lastname: Liu
authors.forename: Pu
authors.initials: P
authors.lastname: Liu
authors.forename: Daniel V Zandt
authors.initials: DV
authors.lastname: Moyer
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D004551:Elbow Joint / Q000502:physiology*; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008875:Middle Aged; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods; D010807:Physical Endurance / Q000502:physiology*; D011187:Posture / Q000502:physiology*; D019415:Torque; D055815:Young Adult