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Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels

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@article{IJASEIT7131,
   author = {Khairul Anam and Adel Al-Jumaily},
   title = {Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {1828--1834},
   keywords = {myoelectric; pattern recognition; dimensionality reduction.},
   abstract = {

The classification accuracy of pattern recognition is determined by the extracted features and the utilized classifiers. Many efforts have been conducted to obtain the best features either by introducing a new feature or proposing a new projection method to increase class separability. Recently, spectral regression extreme learning machine (SRELM) has been introduced to improve the class separability of the features. However, the evaluation of SRELM was only focused on the myoelectric or electromyography pattern recognition from many EMG channels. In practical application, the user is more convenient with less number of channels. Then, the problem is whether the SRELM would be able to work efficiently for less EMG channels. The objective of this paper is to examine the performance of SRELM for bio-signal pattern recognition using two EMG channels. The EMG electrodes were located on flexor policies lounges and flexor digitorium superficial muscles of ten healthy subjects. Various time domain features were involved with various sizes. SRELM will project these features to more recognize features before being feed to multiple classifiers.  Those classifiers are randomized Variable Translation Wavelet Neural Networks (RVT-WNN), extreme learning machine(ELM), support vector machine (SVM), and linear discriminant analysis (LDA). The performance of SREM was compared to other feature methods, such as LDA, uncorrelated LDA (ULDA), orthogonal fuzzy neighborhood dimensionality reduction (OFNDA), and spectral regression discriminant analysis (SRDA). The experimental results show that SRELM performed well when dealing with different class numbers by classification accuracy of around 95.67% for ten class movements and performed better than SRDA.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7131},    doi = {10.18517/ijaseit.10.5.7131} }

EndNote

%A Anam, Khairul
%A Al-Jumaily, Adel
%D 2020
%T Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels
%B 2020
%9 myoelectric; pattern recognition; dimensionality reduction.
%! Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels
%K myoelectric; pattern recognition; dimensionality reduction.
%X 

The classification accuracy of pattern recognition is determined by the extracted features and the utilized classifiers. Many efforts have been conducted to obtain the best features either by introducing a new feature or proposing a new projection method to increase class separability. Recently, spectral regression extreme learning machine (SRELM) has been introduced to improve the class separability of the features. However, the evaluation of SRELM was only focused on the myoelectric or electromyography pattern recognition from many EMG channels. In practical application, the user is more convenient with less number of channels. Then, the problem is whether the SRELM would be able to work efficiently for less EMG channels. The objective of this paper is to examine the performance of SRELM for bio-signal pattern recognition using two EMG channels. The EMG electrodes were located on flexor policies lounges and flexor digitorium superficial muscles of ten healthy subjects. Various time domain features were involved with various sizes. SRELM will project these features to more recognize features before being feed to multiple classifiers.  Those classifiers are randomized Variable Translation Wavelet Neural Networks (RVT-WNN), extreme learning machine(ELM), support vector machine (SVM), and linear discriminant analysis (LDA). The performance of SREM was compared to other feature methods, such as LDA, uncorrelated LDA (ULDA), orthogonal fuzzy neighborhood dimensionality reduction (OFNDA), and spectral regression discriminant analysis (SRDA). The experimental results show that SRELM performed well when dealing with different class numbers by classification accuracy of around 95.67% for ten class movements and performed better than SRDA.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7131 %R doi:10.18517/ijaseit.10.5.7131 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 5 %@ 2088-5334

IEEE

Khairul Anam and Adel Al-Jumaily,"Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 1828-1834, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.7131.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Anam, Khairul
AU  - Al-Jumaily, Adel
PY  - 2020
TI  - Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 1828
EP  - 1834
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - myoelectric; pattern recognition; dimensionality reduction.
N2  - 

The classification accuracy of pattern recognition is determined by the extracted features and the utilized classifiers. Many efforts have been conducted to obtain the best features either by introducing a new feature or proposing a new projection method to increase class separability. Recently, spectral regression extreme learning machine (SRELM) has been introduced to improve the class separability of the features. However, the evaluation of SRELM was only focused on the myoelectric or electromyography pattern recognition from many EMG channels. In practical application, the user is more convenient with less number of channels. Then, the problem is whether the SRELM would be able to work efficiently for less EMG channels. The objective of this paper is to examine the performance of SRELM for bio-signal pattern recognition using two EMG channels. The EMG electrodes were located on flexor policies lounges and flexor digitorium superficial muscles of ten healthy subjects. Various time domain features were involved with various sizes. SRELM will project these features to more recognize features before being feed to multiple classifiers.  Those classifiers are randomized Variable Translation Wavelet Neural Networks (RVT-WNN), extreme learning machine(ELM), support vector machine (SVM), and linear discriminant analysis (LDA). The performance of SREM was compared to other feature methods, such as LDA, uncorrelated LDA (ULDA), orthogonal fuzzy neighborhood dimensionality reduction (OFNDA), and spectral regression discriminant analysis (SRDA). The experimental results show that SRELM performed well when dealing with different class numbers by classification accuracy of around 95.67% for ten class movements and performed better than SRDA.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7131 DO - 10.18517/ijaseit.10.5.7131

RefWorks

RT Journal Article
ID 7131
A1 Anam, Khairul
A1 Al-Jumaily, Adel
T1 Performance Evaluation of SRELM on Bio-signal Pattern Recognition Using Two Electromyography Channels
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 1828
OP 1834
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 myoelectric; pattern recognition; dimensionality reduction.
AB 

The classification accuracy of pattern recognition is determined by the extracted features and the utilized classifiers. Many efforts have been conducted to obtain the best features either by introducing a new feature or proposing a new projection method to increase class separability. Recently, spectral regression extreme learning machine (SRELM) has been introduced to improve the class separability of the features. However, the evaluation of SRELM was only focused on the myoelectric or electromyography pattern recognition from many EMG channels. In practical application, the user is more convenient with less number of channels. Then, the problem is whether the SRELM would be able to work efficiently for less EMG channels. The objective of this paper is to examine the performance of SRELM for bio-signal pattern recognition using two EMG channels. The EMG electrodes were located on flexor policies lounges and flexor digitorium superficial muscles of ten healthy subjects. Various time domain features were involved with various sizes. SRELM will project these features to more recognize features before being feed to multiple classifiers.  Those classifiers are randomized Variable Translation Wavelet Neural Networks (RVT-WNN), extreme learning machine(ELM), support vector machine (SVM), and linear discriminant analysis (LDA). The performance of SREM was compared to other feature methods, such as LDA, uncorrelated LDA (ULDA), orthogonal fuzzy neighborhood dimensionality reduction (OFNDA), and spectral regression discriminant analysis (SRDA). The experimental results show that SRELM performed well when dealing with different class numbers by classification accuracy of around 95.67% for ten class movements and performed better than SRDA.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7131 DO - 10.18517/ijaseit.10.5.7131