System identification of non-linear, dynamic EMG-torque relationship about the elbow

Author

Liu, Lukai ; Liu, Pu ; Moyer, Daniel V. ; Clancy, Edward A.

Author_Institution

Worcester Polytech. Inst., Worcester, MA, USA

fYear

2011

fDate

1-3 April 2011

Firstpage

1

Lastpage

2

Abstract

The surface electromyogram (EMG) from biceps/triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/non-linear model structures and system identification techniques. EMG-torque performance was improved by: advanced (i.e., whitened, multiple-channel) EMGσ processors; longer duration training sets (52 s vs. 26 s); and determination of model parameters via the use of the pseudo-inverse and ridge regression methods. Best performance provided an error of 4.65% maximum voluntary contraction (MVC) flexion.

Keywords

biomechanics; electromyography; estimation theory; medical signal processing; regression analysis; torque; biceps muscles; elbow torque; estimation processors; long duration training sets; maximum voluntary contraction flexion; model parameter determination; nonlinear dynamic EMG-torque relationship; pseudoinverse regression methods; ridge regression methods; surface electromyogram; system identification; triceps muscles; Electromyography; Muscles; Polynomials; Program processors; System identification; Torque; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioengineering Conference (NEBEC), 2011 IEEE 37th Annual Northeast

Conference_Location

Troy, NY

ISSN

2160-7001

Print_ISBN

978-1-61284-827-3

Type

conf

DOI

10.1109/NEBC.2011.5778638

Filename

5778638