Deep Convolution Neural Network to Improve Hand Motion Classification Performance Against Varying Orientation Using Electromyography Signal

High accuracy and fast computation time are essential in implementing hand gesture pattern recognition for prosthetic hand using electromyography (EMG) signal. However, several physical parameters affect the characteristics of the EMG signal, including forearm orientation. Therefore, this study aims to develop a deep learning classifier using convolution neural network (CNN) algorithm that maintains accuracy with forearm orientation changes. The main advantage of this method is the simplicity (without feature extraction process) and able to maintain the accuracy against the orientation changes. This method consists of a two-dimensional convolution, max-pooling, four fully connected and output layer. The input layer classifier received six channels of raw EMG signal derived from ten able bodies. As a comparison, several conventional classifiers including support vector machine, k-nearest neighborhood, linear discriminant analysis and decision tree were applied to examine the performance among the classifiers. The result showed that the accuracy of the proposed CNN classifier based on all orientation outperfomed other classifers (96.8 ± 1.87%). Furthermore, the difference in accuracy among the orientations was less then 5%. This indicates that the classifier is able to maintain high accuracy with changes in orientation. In conclusion, this study is applicable in the development of prosthetic hands using EMG signal as control with constant accuracy when the forearm orientation varies.