Classification Of EMG Signal Based On Time Domain And Frequency Domain Features
Electromyography (EMG) is widely used in controlling the signal in manipulating the robot assisted rehabilitation. In order to manipulate a more accurate robot assisted, the feature extraction and selection were equally important. This study evaluated the performance of time domain (TD) and frequenc...
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Universiti Teknikal Malaysia Melaka (UTeM)
2017
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my.utem.eprints.227822021-08-23T02:11:49Z http://eprints.utem.edu.my/id/eprint/22782/ Classification Of EMG Signal Based On Time Domain And Frequency Domain Features Too, Jing Wei Abdullah, Abdul Rahim Tengku Zawawi, Tengku Nor Shuhada Mohd Saad, Norhashimah Musa, Haslinda T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Electromyography (EMG) is widely used in controlling the signal in manipulating the robot assisted rehabilitation. In order to manipulate a more accurate robot assisted, the feature extraction and selection were equally important. This study evaluated the performance of time domain (TD) and frequency domain (FD) features in discriminating EMG signal. To investigate the features performance, the linear discriminate analysis (LDA) was introduced. The present study showed that the FD features achieved the highest accuracy of 91.34% in LDA. The results were verified by LDA classifier and FD features showed best classification performance in EMG signal classification application. Universiti Teknikal Malaysia Melaka (UTeM) 2017 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/22782/2/Classification%20of%20EMG%20Signal%20Based%20on%20Time%20Domain%20and%20Frequency%20Domain%20Features.pdf Too, Jing Wei and Abdullah, Abdul Rahim and Tengku Zawawi, Tengku Nor Shuhada and Mohd Saad, Norhashimah and Musa, Haslinda (2017) Classification Of EMG Signal Based On Time Domain And Frequency Domain Features. International Journal Of Human And Technology Interaction (IJHaTI), 1 (1). pp. 25-30. ISSN 2590-3551 http://journal.utem.edu.my/index.php/ijhati/article/view/2840 |
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T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Too, Jing Wei Abdullah, Abdul Rahim Tengku Zawawi, Tengku Nor Shuhada Mohd Saad, Norhashimah Musa, Haslinda Classification Of EMG Signal Based On Time Domain And Frequency Domain Features |
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Electromyography (EMG) is widely used in controlling the signal in manipulating the robot assisted rehabilitation. In order to manipulate a more accurate robot assisted, the feature extraction and selection were equally important. This study evaluated the performance of time domain (TD) and frequency domain (FD) features in discriminating EMG signal. To investigate the features performance, the linear discriminate analysis (LDA) was introduced. The present study showed that the FD features achieved the highest accuracy of 91.34% in LDA. The results were verified by LDA classifier and FD features showed best classification performance in EMG signal classification application. |
format |
Article |
author |
Too, Jing Wei Abdullah, Abdul Rahim Tengku Zawawi, Tengku Nor Shuhada Mohd Saad, Norhashimah Musa, Haslinda |
author_facet |
Too, Jing Wei Abdullah, Abdul Rahim Tengku Zawawi, Tengku Nor Shuhada Mohd Saad, Norhashimah Musa, Haslinda |
author_sort |
Too, Jing Wei |
title |
Classification Of EMG Signal Based On Time Domain And Frequency Domain Features |
title_short |
Classification Of EMG Signal Based On Time Domain And Frequency Domain Features |
title_full |
Classification Of EMG Signal Based On Time Domain And Frequency Domain Features |
title_fullStr |
Classification Of EMG Signal Based On Time Domain And Frequency Domain Features |
title_full_unstemmed |
Classification Of EMG Signal Based On Time Domain And Frequency Domain Features |
title_sort |
classification of emg signal based on time domain and frequency domain features |
publisher |
Universiti Teknikal Malaysia Melaka (UTeM) |
publishDate |
2017 |
url |
http://eprints.utem.edu.my/id/eprint/22782/2/Classification%20of%20EMG%20Signal%20Based%20on%20Time%20Domain%20and%20Frequency%20Domain%20Features.pdf http://eprints.utem.edu.my/id/eprint/22782/ http://journal.utem.edu.my/index.php/ijhati/article/view/2840 |
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1709671904296566784 |
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13.159267 |