AUTHOR=Copaci Dorin , Arias Janeth , Gómez-Tomé Marcos , Moreno Luis , Blanco Dolores 

TITLE=sEMG-Based Gesture Classifier for a Rehabilitation Glove

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.750482

DOI=10.3389/fnbot.2022.750482

ISSN=1662-5218

ABSTRACT=Human hand gesture recognition from the superficial electromyography signals is one of the main paradigms for prosthetic and rehabilitation devices control. The accuracy of the gesture recognition is correlated with the control mechanism. In this work, a new classifier based on Bayesian Neural Network, Pattern Recognition Networks and Layer Recurrent Network is presented. Results obtained with this architecture represents a promising solution for the hand gesture recognition (96.3% accuracy) at superficial electromyography signal classification. For real time classification performance with rehabilitation devices, a new simple and efficient interface is developed in which users can re-train the classification algorithm with his own sEMG gesture data in a few minutes while enables a Shape Memory Alloy based rehabilitation device connection and control. The position of reference for the rehabilitation device is generated by the algorithm based on the classifier, which is capable to detect the user movement intention in real time. The main aim of this study is to prove that the device control algorithm is adapted to the characteristics and necessities of the patient through the proposed classifier with high accuracy in hand gesture recognition.