Estimation of hand-grip intention: Cylindrical, spherical and hook using deep neural networks

Abstract

Upper extremities amputations can produce different disability degrees in the amputated person, this is acerbated even more, when it happens during active working life. So, for this reason, it is of social importance the study of prostheses and algorithms that help a better control of these by the user. In this research, we propose an architecture based on recurrent neural networks, called Long Short-Term Memory, and convolutional neural networks for classification of electromyographic signals, with applications for hand prosthesis control. The proposed network classifies three types of movements made by the hand: cylindrical, spherical and hook grips. The proposed model showed an efficiency (accuracy) of 89%, in contrast to an artificial neural network based on completely connected layers that only obtained an efficiency of 80% in the prediction of the hand movements. The present work is limited to evaluate the network with an electromyogram input, the control system for hand prosthesis was not implemented. Thus, an architecture of convolutional networks for the control of hand prostheses that can be trained with the signals of the subject.