sEMG-Based Gesture Classifier for a Rehabilitation Glove - Copaci, Dorin; Arias, Janeth; Gómez-Tomé, Marcos; Moreno, Luis; Blanco, Dolores (Frontiers in Neurorobotics, 2022)
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.