Literature DB >> 9311171

Relating agonist-antagonist electromyograms to joint torque during isometric, quasi-isotonic, nonfatiguing contractions.

E A Clancy1, N Hogan.   

Abstract

This paper describes an experimental study which relates simultaneous elbow flexor-extensor electromyogram (EMG) amplitude to joint torque. Investigation was limited to the case of isometric, quasi-isotonic (slowly force-varying), nonfatiguing contractions. For each of the flexor and extensor muscle groups, the model relationship between muscle group torque contribution and EMG amplitude was constrained to be a sum of basis functions which had a linear dependence on a set of fit parameters. With these constraints, the problem of identifying the EMG-to-torque relationship was reduced to a linear least squares problem. Surface EMG's from elbow flexors and extensors, and joint torque were simultaneously recorded for nonfatiguing, quasi-isotonic, isometric contractions spanning 0-50% maximum voluntary contraction. Single-/multiple-channel unwhitened/whitened/adaptively whitened EMG amplitude processors were used to identify an EMG-to-torque relation, and then estimate joint torque based on this relation. Each unwhitened multiple-channel EMG-to-torque estimator had a standard error (SE) approximately 70% of its respective single-channel estimator. The adaptively whitened multiple-channel joint torque estimator had an SE approximately 90% of the unwhitened multiple-channel estimator, providing an estimation error approximately 3% of the combined flexion/extension torque range. The experimental studies demonstrated that higher fidelity EMG amplitude processing led to improved joint torque estimation.

Mesh:

Year:  1997        PMID: 9311171     DOI: 10.1109/10.634654

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  15 in total

1.  Modeling nonlinear errors in surface electromyography due to baseline noise: a new methodology.

Authors:  Laura Frey Law; Chandramouli Krishnan; Keith Avin
Journal:  J Biomech       Date:  2010-09-25       Impact factor: 2.712

2.  Multivariable dynamic ankle mechanical impedance with relaxed muscles.

Authors:  Hyunglae Lee; Hermano Igo Krebs; Neville Hogan
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-03-26       Impact factor: 3.802

3.  Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force-varying contractions.

Authors:  Edward A Clancy; Oljeta Bida; Denis Rancourt
Journal:  J Biomech       Date:  2005-10-20       Impact factor: 2.712

4.  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

5.  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

6.  Trunk antagonist co-activation is associated with impaired neuromuscular performance.

Authors:  N Peter Reeves; Jacek Cholewicki; Theodore Milner; Angela S Lee
Journal:  Exp Brain Res       Date:  2008-04-29       Impact factor: 1.972

7.  Multivariable dynamic ankle mechanical impedance with active muscles.

Authors:  Hyunglae Lee; Hermano Igo Krebs; Neville Hogan
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-09       Impact factor: 3.802

8.  An Electromyographic-driven Musculoskeletal Torque Model using Neuro-Fuzzy System Identification: A Case Study.

Authors:  Zohreh Jafari; Mehdi Edrisi; Hamid Reza Marateb
Journal:  J Med Signals Sens       Date:  2014-10

9.  A Comparative Approach to Hand Force Estimation using Artificial Neural Networks.

Authors:  Farid Mobasser; Keyvan Hashtrudi-Zaad
Journal:  Biomed Eng Comput Biol       Date:  2012-07-30

10.  A SEMG-Force Estimation Framework Based on a Fast Orthogonal Search Method Coupled with Factorization Algorithms.

Authors:  Xiang Chen; Yuan Yuan; Shuai Cao; Xu Zhang; Xun Chen
Journal:  Sensors (Basel)       Date:  2018-07-11       Impact factor: 3.576

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