Deep neural network classifier for hand movement prediction

The prediction of the hand movement based on Electromyography (EMG) signals has been extensively studied over the past three decades. However, recent EGM applications pose an emerging need of efficient classification of EMG signals. Toward this goal, we propose a deep neural network (DNN) classifier in this study to classify 6 different hand movement from EMG signals. DNN classifier has ability to extract new features from raw data and reduce the dimension of the data set. Our experimental results based on human subjects demonstrate that DNN classifier can efficiently classify the EMG signals to accurately distinguish different hand movement.