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Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger...

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Autores principales: Lee, Kyung Hyun, Min, Ji Young, Byun, Sangwon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749583/
https://www.ncbi.nlm.nih.gov/pubmed/35009768
http://dx.doi.org/10.3390/s22010225
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author Lee, Kyung Hyun
Min, Ji Young
Byun, Sangwon
author_facet Lee, Kyung Hyun
Min, Ji Young
Byun, Sangwon
author_sort Lee, Kyung Hyun
collection PubMed
description Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.
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spelling pubmed-87495832022-01-12 Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks Lee, Kyung Hyun Min, Ji Young Byun, Sangwon Sensors (Basel) Article Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden. MDPI 2021-12-29 /pmc/articles/PMC8749583/ /pubmed/35009768 http://dx.doi.org/10.3390/s22010225 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
Lee, Kyung Hyun
Min, Ji Young
Byun, Sangwon
Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
title Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
title_full Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
title_fullStr Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
title_full_unstemmed Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
title_short Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
title_sort electromyogram-based classification of hand and finger gestures using artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749583/
https://www.ncbi.nlm.nih.gov/pubmed/35009768
http://dx.doi.org/10.3390/s22010225
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