EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS
In this paper, deep learning-based hand gesture recognition using surface EMG signals is presented. We use Principal component analysis (PCA) to reduce the data set. Here a threshold-based approach is also proposed to select the principal components (PCs). Then the Continuous wavelet transform (CWT...
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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2023-09-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/375 |
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| _version_ | 1823858318774370304 |
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| author | SABRINA AKTER BIMAL KUMAR PRAMANIK MD EKRAMUL HAMID |
| author_facet | SABRINA AKTER BIMAL KUMAR PRAMANIK MD EKRAMUL HAMID |
| author_sort | SABRINA AKTER |
| collection | DOAJ |
| description |
In this paper, deep learning-based hand gesture recognition using surface EMG signals is presented. We use Principal component analysis (PCA) to reduce the data set. Here a threshold-based approach is also proposed to select the principal components (PCs). Then the Continuous wavelet transform (CWT) is carried out to prepare the time-frequency representation of images which is used as the input of the classifier. A very deep convolutional neural network (CNN) is proposed as the gesture classifier. The classifier is trained on 10-fold cross-validation framework and we achieve average recognition accuracy of 99.44%, sensitivity of 97.78% and specificity of 99.68% respectively.
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| format | Article |
| id | doaj-art-1b8ee9f5bfc942a690e4ea00e96b2be8 |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-1b8ee9f5bfc942a690e4ea00e96b2be82025-02-11T11:39:21ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322023-09-01292EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLSSABRINA AKTERBIMAL KUMAR PRAMANIKMD EKRAMUL HAMID In this paper, deep learning-based hand gesture recognition using surface EMG signals is presented. We use Principal component analysis (PCA) to reduce the data set. Here a threshold-based approach is also proposed to select the principal components (PCs). Then the Continuous wavelet transform (CWT) is carried out to prepare the time-frequency representation of images which is used as the input of the classifier. A very deep convolutional neural network (CNN) is proposed as the gesture classifier. The classifier is trained on 10-fold cross-validation framework and we achieve average recognition accuracy of 99.44%, sensitivity of 97.78% and specificity of 99.68% respectively. https://jesr.ub.ro/index.php/1/article/view/375EMG, deep learning, CWT, PCA, hand gesture recognition |
| spellingShingle | SABRINA AKTER BIMAL KUMAR PRAMANIK MD EKRAMUL HAMID EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS Journal of Engineering Studies and Research EMG, deep learning, CWT, PCA, hand gesture recognition |
| title | EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS |
| title_full | EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS |
| title_fullStr | EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS |
| title_full_unstemmed | EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS |
| title_short | EMG- BASED HAND GESTURE RECOGNITION USING DEEP LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS |
| title_sort | emg based hand gesture recognition using deep learning and signal to image conversion tools |
| topic | EMG, deep learning, CWT, PCA, hand gesture recognition |
| url | https://jesr.ub.ro/index.php/1/article/view/375 |
| work_keys_str_mv | AT sabrinaakter emgbasedhandgesturerecognitionusingdeeplearningandsignaltoimageconversiontools AT bimalkumarpramanik emgbasedhandgesturerecognitionusingdeeplearningandsignaltoimageconversiontools AT mdekramulhamid emgbasedhandgesturerecognitionusingdeeplearningandsignaltoimageconversiontools |