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Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features
The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognitio...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076314/ https://www.ncbi.nlm.nih.gov/pubmed/35528339 http://dx.doi.org/10.1155/2022/6414664 |
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author | Islam, Md. Johirul Ahmad, Shamim Haque, Fahmida Ibne Reaz, Mamun Bin Bhuiyan, Mohammad A. S. Minhad, Khairun Nisa' Islam, Md. Rezaul |
author_facet | Islam, Md. Johirul Ahmad, Shamim Haque, Fahmida Ibne Reaz, Mamun Bin Bhuiyan, Mohammad A. S. Minhad, Khairun Nisa' Islam, Md. Rezaul |
author_sort | Islam, Md. Johirul |
collection | PubMed |
description | The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance. |
format | Online Article Text |
id | pubmed-9076314 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90763142022-05-07 Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features Islam, Md. Johirul Ahmad, Shamim Haque, Fahmida Ibne Reaz, Mamun Bin Bhuiyan, Mohammad A. S. Minhad, Khairun Nisa' Islam, Md. Rezaul Comput Intell Neurosci Research Article The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance. Hindawi 2022-04-29 /pmc/articles/PMC9076314/ /pubmed/35528339 http://dx.doi.org/10.1155/2022/6414664 Text en Copyright © 2022 Md. Johirul Islam et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Islam, Md. Johirul Ahmad, Shamim Haque, Fahmida Ibne Reaz, Mamun Bin Bhuiyan, Mohammad A. S. Minhad, Khairun Nisa' Islam, Md. Rezaul Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features |
title | Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features |
title_full | Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features |
title_fullStr | Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features |
title_full_unstemmed | Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features |
title_short | Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features |
title_sort | myoelectric pattern recognition performance enhancement using nonlinear features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076314/ https://www.ncbi.nlm.nih.gov/pubmed/35528339 http://dx.doi.org/10.1155/2022/6414664 |
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