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Applying muscle synergy analysis to forearm high-density electromyography of healthy people
INTRODUCTION: Muscle synergy is regarded as a motor control strategy deployed by the central nervous system (CNS). Clarifying the modulation of muscle synergies under different strength training modes is important for the rehabilitation of motor-impaired patients. METHODS: To represent the subtle va...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807910/ https://www.ncbi.nlm.nih.gov/pubmed/36605554 http://dx.doi.org/10.3389/fnins.2022.1067925 |
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author | Geng, Yanjuan Chen, Ziyin Zhao, Yang Cheung, Vincent C. K. Li, Guanglin |
author_facet | Geng, Yanjuan Chen, Ziyin Zhao, Yang Cheung, Vincent C. K. Li, Guanglin |
author_sort | Geng, Yanjuan |
collection | PubMed |
description | INTRODUCTION: Muscle synergy is regarded as a motor control strategy deployed by the central nervous system (CNS). Clarifying the modulation of muscle synergies under different strength training modes is important for the rehabilitation of motor-impaired patients. METHODS: To represent the subtle variation of neuromuscular activities from the smaller forearm muscles during wrist motion, we proposed to apply muscle synergy analysis to preprocessed high-density electromyographic data (HDEMG). Here, modulation of muscle synergies within and across the isometric and isotonic training modes for strengthening muscles across the wrist were investigated. Surface HDEMGs were recorded from healthy subjects (N = 10). Three different HDEMG electrode configurations were used for comparison and validation of the extracted muscle synergies. The cosine of principal angles (CPA) and the Euclidian distance (ED) between synergy vectors were used to evaluate the intra- and inter-mode similarity of muscle synergies. Then, how the activation coefficients modulate the excitation of specific synergy under each mode was examined by pattern recognition. Next, for a closer look at the mode-specific synergies and the synergies shared by the two training modes, k-means clustering was applied. RESULTS: We observed high similarity of muscle synergies across different tasks within each training mode, but decreased similarity of muscle synergies across different training modes. Both intra- and intermode similarity of muscle synergies were consistently robust to electrode configurations regardless of the similarity metric used. DISCUSSION: Overall, our findings suggest that applying muscle synergy analysis to HDEMG is feasible, and that the traditional muscle synergies defined by whole-muscle components may be broadened to include sub-muscle components represented by the HDEMG channels. This work may lead to an appropriate neuromuscular analysis method for motor function evaluation in clinical settings and provide valuable insights for the prescription of rehabilitation training therapies. |
format | Online Article Text |
id | pubmed-9807910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98079102023-01-04 Applying muscle synergy analysis to forearm high-density electromyography of healthy people Geng, Yanjuan Chen, Ziyin Zhao, Yang Cheung, Vincent C. K. Li, Guanglin Front Neurosci Neuroscience INTRODUCTION: Muscle synergy is regarded as a motor control strategy deployed by the central nervous system (CNS). Clarifying the modulation of muscle synergies under different strength training modes is important for the rehabilitation of motor-impaired patients. METHODS: To represent the subtle variation of neuromuscular activities from the smaller forearm muscles during wrist motion, we proposed to apply muscle synergy analysis to preprocessed high-density electromyographic data (HDEMG). Here, modulation of muscle synergies within and across the isometric and isotonic training modes for strengthening muscles across the wrist were investigated. Surface HDEMGs were recorded from healthy subjects (N = 10). Three different HDEMG electrode configurations were used for comparison and validation of the extracted muscle synergies. The cosine of principal angles (CPA) and the Euclidian distance (ED) between synergy vectors were used to evaluate the intra- and inter-mode similarity of muscle synergies. Then, how the activation coefficients modulate the excitation of specific synergy under each mode was examined by pattern recognition. Next, for a closer look at the mode-specific synergies and the synergies shared by the two training modes, k-means clustering was applied. RESULTS: We observed high similarity of muscle synergies across different tasks within each training mode, but decreased similarity of muscle synergies across different training modes. Both intra- and intermode similarity of muscle synergies were consistently robust to electrode configurations regardless of the similarity metric used. DISCUSSION: Overall, our findings suggest that applying muscle synergy analysis to HDEMG is feasible, and that the traditional muscle synergies defined by whole-muscle components may be broadened to include sub-muscle components represented by the HDEMG channels. This work may lead to an appropriate neuromuscular analysis method for motor function evaluation in clinical settings and provide valuable insights for the prescription of rehabilitation training therapies. Frontiers Media S.A. 2022-12-20 /pmc/articles/PMC9807910/ /pubmed/36605554 http://dx.doi.org/10.3389/fnins.2022.1067925 Text en Copyright © 2022 Geng, Chen, Zhao, Cheung and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Geng, Yanjuan Chen, Ziyin Zhao, Yang Cheung, Vincent C. K. Li, Guanglin Applying muscle synergy analysis to forearm high-density electromyography of healthy people |
title | Applying muscle synergy analysis to forearm high-density electromyography of healthy people |
title_full | Applying muscle synergy analysis to forearm high-density electromyography of healthy people |
title_fullStr | Applying muscle synergy analysis to forearm high-density electromyography of healthy people |
title_full_unstemmed | Applying muscle synergy analysis to forearm high-density electromyography of healthy people |
title_short | Applying muscle synergy analysis to forearm high-density electromyography of healthy people |
title_sort | applying muscle synergy analysis to forearm high-density electromyography of healthy people |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807910/ https://www.ncbi.nlm.nih.gov/pubmed/36605554 http://dx.doi.org/10.3389/fnins.2022.1067925 |
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