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Data Augmentation of Surface Electromyography for Hand Gesture Recognition

The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most comm...

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Autores principales: Tsinganos, Panagiotis, Cornelis, Bruno, Cornelis, Jan, Jansen, Bart, Skodras, Athanassios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506981/
https://www.ncbi.nlm.nih.gov/pubmed/32872508
http://dx.doi.org/10.3390/s20174892
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author Tsinganos, Panagiotis
Cornelis, Bruno
Cornelis, Jan
Jansen, Bart
Skodras, Athanassios
author_facet Tsinganos, Panagiotis
Cornelis, Bruno
Cornelis, Jan
Jansen, Bart
Skodras, Athanassios
author_sort Tsinganos, Panagiotis
collection PubMed
description The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.
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spelling pubmed-75069812020-09-30 Data Augmentation of Surface Electromyography for Hand Gesture Recognition Tsinganos, Panagiotis Cornelis, Bruno Cornelis, Jan Jansen, Bart Skodras, Athanassios Sensors (Basel) Article The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved. MDPI 2020-08-29 /pmc/articles/PMC7506981/ /pubmed/32872508 http://dx.doi.org/10.3390/s20174892 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tsinganos, Panagiotis
Cornelis, Bruno
Cornelis, Jan
Jansen, Bart
Skodras, Athanassios
Data Augmentation of Surface Electromyography for Hand Gesture Recognition
title Data Augmentation of Surface Electromyography for Hand Gesture Recognition
title_full Data Augmentation of Surface Electromyography for Hand Gesture Recognition
title_fullStr Data Augmentation of Surface Electromyography for Hand Gesture Recognition
title_full_unstemmed Data Augmentation of Surface Electromyography for Hand Gesture Recognition
title_short Data Augmentation of Surface Electromyography for Hand Gesture Recognition
title_sort data augmentation of surface electromyography for hand gesture recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506981/
https://www.ncbi.nlm.nih.gov/pubmed/32872508
http://dx.doi.org/10.3390/s20174892
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