Deep Learning Based Surface EMG Hand Gesture Classification for Low-Cost Myoelectric Prosthetic Hand

In this paper, a comparative study of classifying different hand gestures of two well-known surface Electromyogram (sEMG) data sets, Rami Khusaba EMG repository, and UCI Machine Learning Repository, is shown. Applying transfer learning and CNN-LSTM neural network architectures, we find out a suitable control scheme for a myoelectric prosthetic hand (we mention it as DUFAB Hand). At first, the continuous wavelet transform (CWT) is exploited to create images from the sEMG signal, which serves as a powerful feature for the classification of different hand gestures. Then, we transferred the learning of various neural nets of image classification, e.g., AlexNet, and ResNet-18 to the sEMG image classification. Application of these deep neural networks outperformed general machine learning techniques with higher accuracy and performance. For example, the combination of CNN and LSTM has achieved the state of the art accuracies for these data sets, of 99.72% for UCI Machine Learning Repository and 99.83% for Rami Khusaba EMG repository respectively. The main contribution of this paper is, establishing an algorithmic pipeline using continuous wavelet transform (CWT) and CNN-LSTM deep neural networks to achieve high accuracy in two sEMG datasets.