EMG- BASED HAND GESTURE RECOGNITION USING DEEP
LEARNING AND SIGNAL-TO-IMAGE CONVERSION TOOLS
SABRINA AKTER, BIMAL KUMAR PRAMANIK, MD EKRAMUL HAMID * Department of Computer Science and Engineering, University of Rajshahi,
Rajshahi-6205, Bangladesh
* Corresponding author, email: ekram_hamid@ru.ac.bd
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.
