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An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking
In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151057/ https://www.ncbi.nlm.nih.gov/pubmed/34064615 http://dx.doi.org/10.3390/s21103311 |
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author | Ballarini, Riccardo Ghislieri, Marco Knaflitz, Marco Agostini, Valentina |
author_facet | Ballarini, Riccardo Ghislieri, Marco Knaflitz, Marco Agostini, Valentina |
author_sort | Ballarini, Riccardo |
collection | PubMed |
description | In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds. |
format | Online Article Text |
id | pubmed-8151057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81510572021-05-27 An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking Ballarini, Riccardo Ghislieri, Marco Knaflitz, Marco Agostini, Valentina Sensors (Basel) Article In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds. MDPI 2021-05-11 /pmc/articles/PMC8151057/ /pubmed/34064615 http://dx.doi.org/10.3390/s21103311 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ballarini, Riccardo Ghislieri, Marco Knaflitz, Marco Agostini, Valentina An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking |
title | An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking |
title_full | An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking |
title_fullStr | An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking |
title_full_unstemmed | An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking |
title_short | An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking |
title_sort | algorithm for choosing the optimal number of muscle synergies during walking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151057/ https://www.ncbi.nlm.nih.gov/pubmed/34064615 http://dx.doi.org/10.3390/s21103311 |
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