Consensus for experimental design in electromyography (CEDE) project: Application of EMG to estimate muscle force.

Consensus Electromyography Motor unit Muscle force

Journal

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
ISSN: 1873-5711
Titre abrégé: J Electromyogr Kinesiol
Pays: England
ID NLM: 9109125

Informations de publication

Date de publication:
14 Jun 2024
Historique:
received: 04 04 2024
revised: 06 06 2024
accepted: 06 06 2024
medline: 29 7 2024
pubmed: 29 7 2024
entrez: 28 7 2024
Statut: aheadofprint

Résumé

Skeletal muscles power movement. Deriving the forces produced by individual muscles has applications across various fields including biomechanics, robotics, and rehabilitation. Since direct in vivo measurement of muscle force in humans is invasive and challenging, its estimation through non-invasive methods such as electromyography (EMG) holds considerable appeal. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, summarizes recommendations on the use of EMG to estimate muscle force. The matrix encompasses the use of bipolar surface EMG, high density surface EMG, and intra-muscular EMG (1) to identify the onset of muscle force during isometric contractions, (2) to identify the offset of muscle force during isometric contractions, (3) to identify force fluctuations during isometric contractions, (4) to estimate force during dynamic contractions, and (5) in combination with musculoskeletal models to estimate force during dynamic contractions. For each application, recommendations on the appropriateness of using EMG to estimate force and justification for each recommendation are provided. The achieved consensus makes clear that there are limited scenarios in which EMG can be used to accurately estimate muscle forces. In most cases, it remains important to consider the activation as well as the muscle state and other biomechanical and physiological factors- such as in the context of a formal mechanical model. This matrix is intended to encourage interdisciplinary discussions regarding the integration of EMG with other experimental techniques and to promote advances in the application of EMG towards developing muscle models and musculoskeletal simulations that can accurately predict muscle forces in healthy and clinical populations.

Identifiants

pubmed: 39069427
pii: S1050-6411(24)00054-3
doi: 10.1016/j.jelekin.2024.102910
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102910

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Taylor J M Dick (T)

School of Biomedical Sciences, The University of Queensland, Brisbane, Australia.

Kylie Tucker (K)

School of Biomedical Sciences, The University of Queensland, Brisbane, Australia.

François Hug (F)

School of Biomedical Sciences, The University of Queensland, Brisbane, Australia; Université Côte d'Azur, LAMHESS, Nice, France.

Manuela Besomi (M)

School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia.

Jaap H van Dieën (JH)

Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands.

Roger M Enoka (RM)

Department of Integrative Physiology, University of Colorado Boulder, CO, USA.

Thor Besier (T)

Auckland Bioengineering Institute and Department of Engineering Science & Biomedical Engineering, University of Auckland, Auckland, New Zealand.

Richard G Carson (RG)

Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland; School of Psychology, Queen's University Belfast, Belfast, UK; School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.

Edward A Clancy (EA)

Worcester Polytechnic Institute, Worcester, MA, USA.

Catherine Disselhorst-Klug (C)

Department of Rehabilitation and Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, Aachen, Germany.

Deborah Falla (D)

Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, UK.

Dario Farina (D)

Department of Bioengineering, Imperial College London, London, UK.

Simon Gandevia (S)

Neuroscience Research Australia, University of New South Wales, Sydney, Australia.

Aleš Holobar (A)

Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, Maribor, Slovenia.

Matthew C Kiernan (MC)

Brain and Mind Centre, University of Sydney, Sydney, Australia; Department of Neurology, Royal Prince Alfred Hospital, Sydney, Australia.

Madeleine Lowery (M)

School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin, Ireland.

Kevin McGill (K)

US Department of Veterans Affairs.

Roberto Merletti (R)

LISiN, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy.

Eric Perreault (E)

Northwestern University, Evanston, IL, USA; Shirley Ryan AbilityLab, Chicago, IL, USA.

John C Rothwell (JC)

Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, London, UK.

Karen Søgaard (K)

Department of Clinical Research and Department of Sports Sciences and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.

Tim Wrigley (T)

Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, University of Melbourne, Parkville, Australia.

Paul W Hodges (PW)

School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia. Electronic address: p.hodges@uq.edu.au.

Classifications MeSH