Literature DB >> 25706722

Influence of Joint Angle on EMG-Torque Model During Constant-Posture, Torque-Varying Contractions.

Pu Liu, Lukai Liu, Edward A Clancy.   

Abstract

Relating the electromyogram (EMG) to joint torque is useful in various application areas, including prosthesis control, ergonomics and clinical biomechanics. Limited study has related EMG to torque across varied joint angles, particularly when subjects performed force-varying contractions or when optimized modeling methods were utilized. We related the biceps-triceps surface EMG of 22 subjects to elbow torque at six joint angles (spanning 60° to 135°) during constant-posture, torque-varying contractions. Three nonlinear EMG σ -torque models, advanced EMG amplitude (EMG σ ) estimation processors (i.e., whitened, multiple-channel) and the duration of data used to train models were investigated. When EMG-torque models were formed separately for each of the six distinct joint angles, a minimum "gold standard" error of 4.01±1.2% MVC(F90) 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 achieved a statistically equivalent error of 4.06±1.2% MVC(F90). Results demonstrated that advanced EMG σ processors lead to improved joint torque estimation as do longer model training durations.

Mesh:

Year:  2015        PMID: 25706722     DOI: 10.1109/TNSRE.2015.2405765

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  6 in total

1.  Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes.

Authors:  Edward A Clancy; Carlos Martinez-Luna; Marek Wartenberg; Chenyun Dai; Todd R Farrell
Journal:  J Electromyogr Kinesiol       Date:  2017-03-29       Impact factor: 2.368

2.  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

3.  EMG-Force and EMG-Target Models During Force-Varying Bilateral Hand-Wrist Contraction in Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Carlos Martinez-Luna; Jianan Li; Benjamin E McDonald; Chenyun Dai; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-01-28       Impact factor: 3.802

4.  Real-time, simultaneous myoelectric control using a convolutional neural network.

Authors:  Ali Ameri; Mohammad Ali Akhaee; Erik Scheme; Kevin Englehart
Journal:  PLoS One       Date:  2018-09-13       Impact factor: 3.240

5.  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

6.  Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Jianan Li; William J Boyd; Carlos Martinez-Luna; Chenyun Dai; Haopeng Wang; He Wang; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2022-04-11       Impact factor: 4.528

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.