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Optimal strategy of sEMG feature and measurement position for grasp force estimation

Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it w...

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Autores principales: Wu, Changcheng, Cao, Qingqing, Fei, Fei, Yang, Dehua, Xu, Baoguo, Zhang, Guanglie, Zeng, Hong, Song, Aiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009426/
https://www.ncbi.nlm.nih.gov/pubmed/33784334
http://dx.doi.org/10.1371/journal.pone.0247883
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author Wu, Changcheng
Cao, Qingqing
Fei, Fei
Yang, Dehua
Xu, Baoguo
Zhang, Guanglie
Zeng, Hong
Song, Aiguo
author_facet Wu, Changcheng
Cao, Qingqing
Fei, Fei
Yang, Dehua
Xu, Baoguo
Zhang, Guanglie
Zeng, Hong
Song, Aiguo
author_sort Wu, Changcheng
collection PubMed
description Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it will increase the computational burden. In this paper, an approach based on analysis of variance (ANOVA) and generalized regression neural network (GRNN) for optimal measurement positions and features is proposed, with the purpose of using fewer measurement positions or features to achieve higher estimation accuracy. Firstly, we captured six channels of sEMG from subjects’ forearm and grasp force synchronously. Then, four kinds of features in time domain are extracted from each channel of sEMG. By combining different measurement position sets (MPSs) and feature set (FSs), we construct 945 data sets. These data sets are fed to GRNN to realize grasp force estimation. Normalized root mean square error (NRMS), normalized mean of absolute error (NMAE), and correlation coefficient (CC) between estimated grasp force and actual force are introduced to evaluate the performance of grasp force estimation. Finally, ANOVA and Tukey HSD testing are introduced to analyze grasp force estimation results so as to obtain the optimal measurement positions and features. We obtain the optimal MPSs for grasp force estimation when different FSs are employed, and the optimal FSs when different MPSs are utilized.
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spelling pubmed-80094262021-04-07 Optimal strategy of sEMG feature and measurement position for grasp force estimation Wu, Changcheng Cao, Qingqing Fei, Fei Yang, Dehua Xu, Baoguo Zhang, Guanglie Zeng, Hong Song, Aiguo PLoS One Research Article Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it will increase the computational burden. In this paper, an approach based on analysis of variance (ANOVA) and generalized regression neural network (GRNN) for optimal measurement positions and features is proposed, with the purpose of using fewer measurement positions or features to achieve higher estimation accuracy. Firstly, we captured six channels of sEMG from subjects’ forearm and grasp force synchronously. Then, four kinds of features in time domain are extracted from each channel of sEMG. By combining different measurement position sets (MPSs) and feature set (FSs), we construct 945 data sets. These data sets are fed to GRNN to realize grasp force estimation. Normalized root mean square error (NRMS), normalized mean of absolute error (NMAE), and correlation coefficient (CC) between estimated grasp force and actual force are introduced to evaluate the performance of grasp force estimation. Finally, ANOVA and Tukey HSD testing are introduced to analyze grasp force estimation results so as to obtain the optimal measurement positions and features. We obtain the optimal MPSs for grasp force estimation when different FSs are employed, and the optimal FSs when different MPSs are utilized. Public Library of Science 2021-03-30 /pmc/articles/PMC8009426/ /pubmed/33784334 http://dx.doi.org/10.1371/journal.pone.0247883 Text en © 2021 Wu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Changcheng
Cao, Qingqing
Fei, Fei
Yang, Dehua
Xu, Baoguo
Zhang, Guanglie
Zeng, Hong
Song, Aiguo
Optimal strategy of sEMG feature and measurement position for grasp force estimation
title Optimal strategy of sEMG feature and measurement position for grasp force estimation
title_full Optimal strategy of sEMG feature and measurement position for grasp force estimation
title_fullStr Optimal strategy of sEMG feature and measurement position for grasp force estimation
title_full_unstemmed Optimal strategy of sEMG feature and measurement position for grasp force estimation
title_short Optimal strategy of sEMG feature and measurement position for grasp force estimation
title_sort optimal strategy of semg feature and measurement position for grasp force estimation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009426/
https://www.ncbi.nlm.nih.gov/pubmed/33784334
http://dx.doi.org/10.1371/journal.pone.0247883
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