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Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals
In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhan...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243223/ https://www.ncbi.nlm.nih.gov/pubmed/35782048 http://dx.doi.org/10.3389/fnhum.2022.911204 |
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author | Peng, Fulai Chen, Cai Lv, Danyang Zhang, Ningling Wang, Xingwei Zhang, Xikun Wang, Zhiyong |
author_facet | Peng, Fulai Chen, Cai Lv, Danyang Zhang, Ningling Wang, Xingwei Zhang, Xikun Wang, Zhiyong |
author_sort | Peng, Fulai |
collection | PubMed |
description | In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods. |
format | Online Article Text |
id | pubmed-9243223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92432232022-07-01 Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals Peng, Fulai Chen, Cai Lv, Danyang Zhang, Ningling Wang, Xingwei Zhang, Xikun Wang, Zhiyong Front Hum Neurosci Human Neuroscience In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods. Frontiers Media S.A. 2022-06-16 /pmc/articles/PMC9243223/ /pubmed/35782048 http://dx.doi.org/10.3389/fnhum.2022.911204 Text en Copyright © 2022 Peng, Chen, Lv, Zhang, Wang, Zhang and Wang. 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 | Human Neuroscience Peng, Fulai Chen, Cai Lv, Danyang Zhang, Ningling Wang, Xingwei Zhang, Xikun Wang, Zhiyong Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals |
title | Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals |
title_full | Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals |
title_fullStr | Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals |
title_full_unstemmed | Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals |
title_short | Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals |
title_sort | gesture recognition by ensemble extreme learning machine based on surface electromyography signals |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243223/ https://www.ncbi.nlm.nih.gov/pubmed/35782048 http://dx.doi.org/10.3389/fnhum.2022.911204 |
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