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Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model

To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction...

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Autores principales: Qin, Zixuan, Stapornchaisit, Sorawit, He, Zixun, Yoshimura, Natsue, Koike, Yasuharu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366416/
https://www.ncbi.nlm.nih.gov/pubmed/34408635
http://dx.doi.org/10.3389/fnbot.2021.685961
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author Qin, Zixuan
Stapornchaisit, Sorawit
He, Zixun
Yoshimura, Natsue
Koike, Yasuharu
author_facet Qin, Zixuan
Stapornchaisit, Sorawit
He, Zixun
Yoshimura, Natsue
Koike, Yasuharu
author_sort Qin, Zixuan
collection PubMed
description To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction of joint angles in three degrees of freedom (3-DOFs, include two wrist joint motion and one finger joint motion), and five-fold cross validation was used to evaluate the correlation coefficient (CC). The CC value results of wrist flexion/extension motion obtained from 10 participants was 0.87–0.92, pronation/supination motion was 0.72–0.95, and hand grip/open motion was 0.75–0.94. We backtracked the fully connected layer weights to create a geometry plot for analyzing the motion pattern to investigate the learning of the proposed model. In order to discuss the daily updateability of the model by transfer learning, we performed a second experiment on five of the participants in another day and conducted transfer learning based on smaller amount of dataset. The CC results improved (wrist flexion/extension was 0.90–0.97, pronation/supination was 0.84–0.96, hand grip/open was 0.85–0.92), suggesting the effectiveness of the transfer learning by incorporating the small amounts of sEMG data acquired in different days. We compared our CNN-based model with four conventional regression models, the result illustrates that proposed model significantly outperforms the four conventional models with and without transfer learning. The offline result suggests the reliability of the proposed model in real-time control in different days, it can be applied for real-time prosthetic control in the future.
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spelling pubmed-83664162021-08-17 Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model Qin, Zixuan Stapornchaisit, Sorawit He, Zixun Yoshimura, Natsue Koike, Yasuharu Front Neurorobot Neuroscience To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction of joint angles in three degrees of freedom (3-DOFs, include two wrist joint motion and one finger joint motion), and five-fold cross validation was used to evaluate the correlation coefficient (CC). The CC value results of wrist flexion/extension motion obtained from 10 participants was 0.87–0.92, pronation/supination motion was 0.72–0.95, and hand grip/open motion was 0.75–0.94. We backtracked the fully connected layer weights to create a geometry plot for analyzing the motion pattern to investigate the learning of the proposed model. In order to discuss the daily updateability of the model by transfer learning, we performed a second experiment on five of the participants in another day and conducted transfer learning based on smaller amount of dataset. The CC results improved (wrist flexion/extension was 0.90–0.97, pronation/supination was 0.84–0.96, hand grip/open was 0.85–0.92), suggesting the effectiveness of the transfer learning by incorporating the small amounts of sEMG data acquired in different days. We compared our CNN-based model with four conventional regression models, the result illustrates that proposed model significantly outperforms the four conventional models with and without transfer learning. The offline result suggests the reliability of the proposed model in real-time control in different days, it can be applied for real-time prosthetic control in the future. Frontiers Media S.A. 2021-08-02 /pmc/articles/PMC8366416/ /pubmed/34408635 http://dx.doi.org/10.3389/fnbot.2021.685961 Text en Copyright © 2021 Qin, Stapornchaisit, He, Yoshimura and Koike. 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
Qin, Zixuan
Stapornchaisit, Sorawit
He, Zixun
Yoshimura, Natsue
Koike, Yasuharu
Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model
title Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model
title_full Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model
title_fullStr Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model
title_full_unstemmed Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model
title_short Multi–Joint Angles Estimation of Forearm Motion Using a Regression Model
title_sort multi–joint angles estimation of forearm motion using a regression model
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366416/
https://www.ncbi.nlm.nih.gov/pubmed/34408635
http://dx.doi.org/10.3389/fnbot.2021.685961
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