Deep Neural Network Approach for Hand, Wrist, Grasping and Functional Movements Classification using Lowcost sEMG Sensors This paper presents a Deep Neural Network approach for the classification of 41 hand, wrist, grasping and functional movements based on a public dataset of lowcost sEMG sensors called Ninapro DB5. The recent advancements in sensor technology, mechatronics, signal processing techniques and edge computing hardware equipped with GPU make dexterous prosthetic hands with noninvasive sEMG sensors and control capabilities of machine learning possible. However, its high cost means the technology is not accessible to most people. Therefore, the objective of this paper is to investigate the control and capabilities of a lowcost setup of sEMG sensors for a prosthetic hand. The acquisition setup includes two Thalmic Myo armbands for the total of 16 channels with the sampling rate of 200 Hz. Our approach achieved an overall accuracy of 91% with a macro recall of 77% for the classification of 41 movements, outperformed other algorithms such as SVM, Random forest, and XGBoost. These results suggest that a development of practical prosthetic hand could be possible with lowcost sEMG sensors.

Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network Surface electromyographic (sEMG) signals can be used in medicine, rehabilitation, robotics and industrial fields. This paper proposes a recognition method of finger motions for dexterous control of prosthetic hands. In this work, we record the corresponding sEMG signals from five subjects, and then set up the model by data preprocessing, feature extraction and classification. The results show that high recognition accuracy can be achieved by using time domain feature extraction and artificial neural network. It means that finger motions can be decoded accurately. To acquire the tradeoff between the number of channels and the recognition accuracy, we apply channel number reduction and it is found that the number of channels is at least 7 with recognition accuracy of 90.52%.