Recognition of hand gestures based on emg signals with deep and double-deep q-networks
Keywords: electromyography, hand gesture recognition, reinforcement learning, Deep Q-Network, Double-Deep Q-Network
Abstract
In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals have been of considerable interest in developing human–machine interfaces. Most state-of-the-art HGR approaches are based mainly on supervised machine learning (ML). However, the use of reinforcement learning (RL) techniques to classify EMGs is still a new and open research topic. Methods based on RL have some advantages such as promising classification performance and online learning from the user’s experience. In this work, we propose a user-specific HGR system based on an RL-based agent that learns to characterize EMG signals from five different hand gestures using Deep Q-network (DQN) and Double-Deep Q-Network (Double-DQN) algorithms. Both methods use a feed-forward artificial neural network (ANN) for the representation of the agent policy. We also performed additional tests by adding a long–short-term memory (LSTM) layer to the ANN to analyze and compare its performance. We performed experiments using training, validation, and test sets from our public dataset, EMG-EPN-612. The final accuracy results demonstrate that the best model was DQN without LSTM, obtaining classification and recognition accuracies of up to 90.37%±10.7% and 82.52%±10.9% , respectively. The results obtained in this work demonstrate that RL methods such as DQN and Double-DQN can obtain promising results for classification and recognition problems based on EMG signals.
Más información
Título de la Revista: | SENSORS |
Volumen: | 23 |
Número: | 8 |
Editorial: | MDPI |
Fecha de publicación: | 2023 |
Página de inicio: | 3905 |
Idioma: | English |
URL: | https://www.mdpi.com/1424-8220/23/8/3905 |