A Deep Q-Network based hand gesture recognition system for control of robotic platforms

Patricio J. Cruz, Juan Pablo Vásconez, Ricardo Romero, Alex Chico, Marco E. Benalcázar, Robin Álvarez, Lorena Isabel Barona López, Ángel Leonardo Valdivieso Caraguay

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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. The information obtained from the HGR systems has the potential to be helpful to control machines such as video games, vehicles, and even robots. Therefore, the key idea of the HGR system is to identify the moment in which a hand gesture was performed and it’s class. Several human-machine state-of-the-art approaches use supervised machine learning (ML) techniques for the HGR system. However, the use of reinforcement learning (RL) approaches to build HGR systems for human-machine interfaces is still an open problem. This work presents a reinforcement learning (RL) approach to classify EMG-IMU signals obtained using a Myo Armband sensor. 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. The HGR proposed system accuracy reaches up to 97.45 ± 1.02 % and 88.05 ± 3.10 % for classification and recognition respectively, with an average inference time per window observation of 20 ms. and we also demonstrate that our method outperforms other approaches in the literature. Then, we test the HGR system to control two different robotic platforms. The first is a three-degrees-of-freedom (DOF) tandem helicopter test bench, and the second is a virtual six-degree-of-freedom (DOF) UR5 robot. We employ the designed hand gesture recognition (HGR) system and the inertial measurement unit (IMU) integrated into the Myo sensor to command and control the motion of both platforms. The movement of the helicopter test bench and the UR5 robot is controlled under a PID controller scheme. Experimental results show the effectiveness of using the proposed HGR system based on DQN for controlling both platforms with a fast and accurate response.

Original languageEnglish
Article number7956
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - Dec 2023

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'A Deep Q-Network based hand gesture recognition system for control of robotic platforms'. Together they form a unique fingerprint.

Cite this