A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs.
artificial neural network
convex optimization
electromyography
linear regression
load sharing
Journal
Frontiers in physiology
ISSN: 1664-042X
Titre abrégé: Front Physiol
Pays: Switzerland
ID NLM: 101549006
Informations de publication
Date de publication:
2023
2023
Historique:
received:
14
11
2022
accepted:
01
02
2023
entrez:
16
3
2023
pubmed:
17
3
2023
medline:
17
3
2023
Statut:
epublish
Résumé
Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (
Identifiants
pubmed: 36923291
doi: 10.3389/fphys.2023.1098225
pii: 1098225
pmc: PMC10009160
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1098225Informations de copyright
Copyright © 2023 Shirzadi, Marateb, Rojas-Martínez, Mansourian, Botter, Vieira dos Anjos, Martins Vieira and Mañanas.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer RM declared a shared affiliation with the authors AB & TV to the handling editor at the time of review.
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