An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter

High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neural network (CNN) algorithm for classifying ten hand motion from two raw EMG signals. The main contribution of this study is the simplicity of pre-processing stage in classifier machine. For instance, the feature extraction process is not required. Furthermore, the performance of the classifier was improved by evaluating the best hyperparameter in deep learning architecture. To validate the performance of deep learning, the public dataset from ten subjects was evaluated. The performance of the proposed method was compared to other conventional machine learning, specifically LDA, SVM, and KNN. The CNN can discriminate the ten hand-motion based on raw EMG signal without handcrafts feature extraction. The results of the evaluation showed that CNN outperformed other classifiers. The average accuracy for all motion ranges between 0.77 and 0.93. The statistical t-test between using two-channel(CH1 and CH2) and single-channel(CH2) shows that there is no significant difference in accuracy with p-value >0.05. The proposed method was useful in the study of prosthetic hand, which required the simple architecture of machine learning and high performance in the classification.