Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks

Abstract

This research compares classification accuracy obtained with the classical classification techniques and the presented convolutional neural network for the recognition of hand gestures used in robotic prostheses for transradial amputees using surface electromyography (sEMG) signals. The first two classifiers are the most used in the literature: support vector machines (SVM) and artificial neural networks (ANN). A new convolutional neural network (CNN) architecture based on the AtzoriNet network is proposed to assess performance according to amputation-related variables. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods and The performance it is compared with other CNN proposed by other authors. The performance of the CNN is evaluated with different metrics, providing good results compared to those proposed by other authors in the literature.