Desarrollo de un Modelo de Reconocimiento de Gestos de la Mano en Tiempo Real Utilizando QNN y Señales Electromiográficas.

Abstract

Hand gesture recognition (HGR) has enabled the development of alternative forms of human-machine interaction in recent years. Typically, HGR models based on supervised learning have been developed with high accuracy. However, over time, the need to add new gestures appears. Consequently, it is necessary to establish a model that is able to adapt to this change. Reinforcement learning is a type of machine learning that permits developing agents capable of adapting their behavior to dynamic environments. In this work, we use Double Deep-Q Networks (DDQN), a reinforcement learning algorithm, to build an agent (a HGR model in this case), based on electromyography signals (EMGs), capable of recognizing new gestures over time. The proposed model is able to recognize 5 hand gestures, achieving an accuracy of 97.36% for classification and 94.83% for recognition. It can be retrained over time with new samples, recalibrating its accuracy and keeping it constant.