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Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions
A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138131/ https://www.ncbi.nlm.nih.gov/pubmed/35634122 http://dx.doi.org/10.7717/peerj-cs.949 |
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author | Thiamchoo, Nantarika Phukpattaranont, Pornchai |
author_facet | Thiamchoo, Nantarika Phukpattaranont, Pornchai |
author_sort | Thiamchoo, Nantarika |
collection | PubMed |
description | A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object grasp classification. These represent feature projections that are combinations of either linear or nonlinear, and supervised or unsupervised types. All pairs of the four types of feature projection with seven types of classifiers were evaluated, with data from six EMG channels and an IMU sensors for nine upper limb positions in the transverse plane. The results showed that SRELM outperformed LDA with supervised feature projections, and t-SNE was superior to PCA with unsupervised feature projections. The classification errors from SRELM and t-SNE paired with the seven classifiers were from 1.50% to 2.65% and from 1.27% to 17.15%, respectively. A one-way ANOVA test revealed no statistically significant difference by classifier type when using the SRELM projection, which is a nonlinear supervised feature projection (p = 0.334). On the other hand, we have to carefully select an appropriate classifier for use with t-SNE, which is a nonlinear unsupervised feature projection. We achieved the lowest classification error 1.27% using t-SNE paired with a k-nearest neighbors classifier. For SRELM, the lowest 1.50% classification error was obtained when paired with a neural network classifier. |
format | Online Article Text |
id | pubmed-9138131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91381312022-05-28 Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions Thiamchoo, Nantarika Phukpattaranont, Pornchai PeerJ Comput Sci Human-Computer Interaction A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object grasp classification. These represent feature projections that are combinations of either linear or nonlinear, and supervised or unsupervised types. All pairs of the four types of feature projection with seven types of classifiers were evaluated, with data from six EMG channels and an IMU sensors for nine upper limb positions in the transverse plane. The results showed that SRELM outperformed LDA with supervised feature projections, and t-SNE was superior to PCA with unsupervised feature projections. The classification errors from SRELM and t-SNE paired with the seven classifiers were from 1.50% to 2.65% and from 1.27% to 17.15%, respectively. A one-way ANOVA test revealed no statistically significant difference by classifier type when using the SRELM projection, which is a nonlinear supervised feature projection (p = 0.334). On the other hand, we have to carefully select an appropriate classifier for use with t-SNE, which is a nonlinear unsupervised feature projection. We achieved the lowest classification error 1.27% using t-SNE paired with a k-nearest neighbors classifier. For SRELM, the lowest 1.50% classification error was obtained when paired with a neural network classifier. PeerJ Inc. 2022-05-06 /pmc/articles/PMC9138131/ /pubmed/35634122 http://dx.doi.org/10.7717/peerj-cs.949 Text en © 2022 Thiamchoo and Phukpattaranont https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Human-Computer Interaction Thiamchoo, Nantarika Phukpattaranont, Pornchai Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
title | Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
title_full | Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
title_fullStr | Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
title_full_unstemmed | Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
title_short | Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
title_sort | evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions |
topic | Human-Computer Interaction |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138131/ https://www.ncbi.nlm.nih.gov/pubmed/35634122 http://dx.doi.org/10.7717/peerj-cs.949 |
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