Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal
Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper prop...
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| Autor*in: |
Phukpattaranont, Pornchai [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer Berlin Heidelberg, 1977, 56(2018), 12 vom: 18. Juni, Seite 2259-2271 |
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| Übergeordnetes Werk: |
volume:56 ; year:2018 ; number:12 ; day:18 ; month:06 ; pages:2259-2271 |
| Links: |
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| DOI / URN: |
10.1007/s11517-018-1857-5 |
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| Katalog-ID: |
OLC2038699046 |
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| 245 | 1 | 0 | |a Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal |
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| 520 | |a Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier | ||
| 650 | 4 | |a Electromyography (EMG) | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a Dimensionality reduction | |
| 650 | 4 | |a Finger movement classification | |
| 650 | 4 | |a EMG pattern recognition | |
| 700 | 1 | |a Thongpanja, Sirinee |4 aut | |
| 700 | 1 | |a Anam, Khairul |4 aut | |
| 700 | 1 | |a Al-Jumaily, Adel |4 aut | |
| 700 | 1 | |a Limsakul, Chusak |4 aut | |
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10.1007/s11517-018-1857-5 doi (DE-627)OLC2038699046 (DE-He213)s11517-018-1857-5-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Phukpattaranont, Pornchai verfasserin (orcid)0000-0003-0885-0176 aut Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2018 Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier Electromyography (EMG) Feature extraction Dimensionality reduction Finger movement classification EMG pattern recognition Thongpanja, Sirinee aut Anam, Khairul aut Al-Jumaily, Adel aut Limsakul, Chusak aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 56(2018), 12 vom: 18. Juni, Seite 2259-2271 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:56 year:2018 number:12 day:18 month:06 pages:2259-2271 https://doi.org/10.1007/s11517-018-1857-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 56 2018 12 18 06 2259-2271 |
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10.1007/s11517-018-1857-5 doi (DE-627)OLC2038699046 (DE-He213)s11517-018-1857-5-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Phukpattaranont, Pornchai verfasserin (orcid)0000-0003-0885-0176 aut Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2018 Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier Electromyography (EMG) Feature extraction Dimensionality reduction Finger movement classification EMG pattern recognition Thongpanja, Sirinee aut Anam, Khairul aut Al-Jumaily, Adel aut Limsakul, Chusak aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 56(2018), 12 vom: 18. Juni, Seite 2259-2271 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:56 year:2018 number:12 day:18 month:06 pages:2259-2271 https://doi.org/10.1007/s11517-018-1857-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 56 2018 12 18 06 2259-2271 |
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10.1007/s11517-018-1857-5 doi (DE-627)OLC2038699046 (DE-He213)s11517-018-1857-5-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Phukpattaranont, Pornchai verfasserin (orcid)0000-0003-0885-0176 aut Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2018 Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier Electromyography (EMG) Feature extraction Dimensionality reduction Finger movement classification EMG pattern recognition Thongpanja, Sirinee aut Anam, Khairul aut Al-Jumaily, Adel aut Limsakul, Chusak aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 56(2018), 12 vom: 18. Juni, Seite 2259-2271 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:56 year:2018 number:12 day:18 month:06 pages:2259-2271 https://doi.org/10.1007/s11517-018-1857-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 56 2018 12 18 06 2259-2271 |
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10.1007/s11517-018-1857-5 doi (DE-627)OLC2038699046 (DE-He213)s11517-018-1857-5-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Phukpattaranont, Pornchai verfasserin (orcid)0000-0003-0885-0176 aut Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2018 Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier Electromyography (EMG) Feature extraction Dimensionality reduction Finger movement classification EMG pattern recognition Thongpanja, Sirinee aut Anam, Khairul aut Al-Jumaily, Adel aut Limsakul, Chusak aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 56(2018), 12 vom: 18. Juni, Seite 2259-2271 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:56 year:2018 number:12 day:18 month:06 pages:2259-2271 https://doi.org/10.1007/s11517-018-1857-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 56 2018 12 18 06 2259-2271 |
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10.1007/s11517-018-1857-5 doi (DE-627)OLC2038699046 (DE-He213)s11517-018-1857-5-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Phukpattaranont, Pornchai verfasserin (orcid)0000-0003-0885-0176 aut Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2018 Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier Electromyography (EMG) Feature extraction Dimensionality reduction Finger movement classification EMG pattern recognition Thongpanja, Sirinee aut Anam, Khairul aut Al-Jumaily, Adel aut Limsakul, Chusak aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 56(2018), 12 vom: 18. Juni, Seite 2259-2271 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:56 year:2018 number:12 day:18 month:06 pages:2259-2271 https://doi.org/10.1007/s11517-018-1857-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 56 2018 12 18 06 2259-2271 |
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Phukpattaranont, Pornchai @@aut@@ Thongpanja, Sirinee @@aut@@ Anam, Khairul @@aut@@ Al-Jumaily, Adel @@aut@@ Limsakul, Chusak @@aut@@ |
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evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal |
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Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal |
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Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier © International Federation for Medical and Biological Engineering 2018 |
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Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier © International Federation for Medical and Biological Engineering 2018 |
| abstract_unstemmed |
Abstract Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstractMean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier © International Federation for Medical and Biological Engineering 2018 |
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