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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2023-09-01
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| Series: | Journal of Engineering Studies and Research |
| Subjects: | |
| Online Access: | https://jesr.ub.ro/index.php/1/article/view/375 |
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| Summary: | 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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| ISSN: | 2068-7559 2344-4932 |