For this, we create an agent based on the Deep Q-learning algorithm (DQN) to learn a policy from online experiences to classify EMG-IMU signals. This work presents a reinforcement learning (RL) approach to classify EMG-IMU signals obtained using a Myo Armband sensor. However, the use of reinforcement learning (RL) approaches to build HGR systems for human-machine interfaces is still an open problem. Several human-machine state-of-the-art approaches use supervised machine learning (ML) techniques for the HGR system.
Therefore, the key idea of the HGR system is to identify the moment in which a hand gesture was performed and it’s class. The information obtained from the HGR systems has the potential to be helpful to control machines such as video games, vehicles, and even robots. Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years.
