Literature DB >> 23932797

Influence of joint angle on EMG-torque model during constant-posture, quasi-constant-torque contractions.

Pu Liu1, Lukai Liu, Francois Martel, Denis Rancourt, Edward A Clancy.   

Abstract

Electromyogram (EMG)-torque modeling is of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. One important aspect of EMG-torque modeling is the ability to account for the joint angle influence. This manuscript describes an experimental study which relates the biceps/triceps surface EMG of 12 subjects to elbow torque at seven joint angles (spanning 45-135°) during constant-posture, quasi-constant-torque contractions. Advanced EMG amplitude (EMGσ) estimation processors (i.e., whitened, multiple-channel) were investigated and three non-linear EMGσ-torque models were evaluated. When EMG-torque models were formed separately for each of the seven distinct joint angles, a minimum "gold standard" error of 4.23±2.2% MVCF90 resulted (i.e., error relative to maximum voluntary contraction at 90° flexion). This model structure, however, did not directly facilitate interpolation across angles. The best model which did so (i.e., parameterized the angle dependence), achieved an error of 4.17±1.7% MVCF90. Results demonstrated that advanced EMGσ processors lead to improved joint torque estimation. We also contrasted models that did vs. did not account for antagonist muscle co-contraction. Models that accounted for co-contraction estimated individual flexion muscle torques that were ∼29% higher and individual extension muscle torques that were ∼68% higher.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Keywords:  Biological system modeling; EMG amplitude estimation; EMG signal processing; Electromyography; Joint angle influence

Mesh:

Year:  2013        PMID: 23932797     DOI: 10.1016/j.jelekin.2013.06.011

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  3 in total

1.  Two degrees of freedom, dynamic, hand-wrist EMG-force using a minimum number of electrodes.

Authors:  Chenyun Dai; Ziling Zhu; Carlos Martinez-Luna; Thane R Hunt; Todd R Farrell; Edward A Clancy
Journal:  J Electromyogr Kinesiol       Date:  2019-04-16       Impact factor: 2.368

2.  Leveraging Joint Mechanics Simplifies the Neural Control of Movement.

Authors:  Daniel Ludvig; Mariah W Whitmore; Eric J Perreault
Journal:  Front Integr Neurosci       Date:  2022-03-21

3.  Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability Models.

Authors:  Tsair-Fwu Lee; Wei-Chun Lin; Hung-Yu Wang; Shu-Yuan Lin; Li-Fu Wu; Shih-Sian Guo; Hsiang-Jui Huang; Hui-Min Ting; Pei-Ju Chao
Journal:  Biomed Res Int       Date:  2015-08-25       Impact factor: 3.411

  3 in total

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