TY - GEN
T1 - Real-Time Robot Reach-To-Grasp Movements Control Via EOG and EMG Signals Decoding
AU - Specht, Bernhard
AU - Tayeb, Zied
AU - Dean, Emannual
AU - Soroushmojdehi, Rahil
AU - Cheng, Gordon
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - In this paper, we propose a real-time human-robot interface (HRI) system, where Electrooculography (EOG) and Electromyography (EMG) signals were decoded to perform reach-to-grasp movements. For that, five different eye movements (up, down, left, right and rest) were classified in real-time and translated into commands to steer an industrial robot (UR-10) to one of the four approximate target directions. Thereafter, EMG signals were decoded to perform the grasping task using an attached gripper to the UR-10 robot arm. The proposed system was tested offline on three different healthy subjects, and mean validation accuracy of 93.62% and 99.50% were obtained across the three subjects for EOG and EMG decoding, respectively. Furthermore, the system was successfully tested in real-time with one subject, and mean online accuracy of 91.66% and 100% were achieved for EOG and EMG decoding, respectively. Our results obtained by combining real-time decoding of EOG and EMG signals for robot control show overall the potential of this approach to develop powerful and less complex HRI systems. Overall, this work provides a proof-of-concept for successful real-time control of robot arms using EMG and EOG signals, paving the way for the development of more dexterous and human-controlled assistive devices.
AB - In this paper, we propose a real-time human-robot interface (HRI) system, where Electrooculography (EOG) and Electromyography (EMG) signals were decoded to perform reach-to-grasp movements. For that, five different eye movements (up, down, left, right and rest) were classified in real-time and translated into commands to steer an industrial robot (UR-10) to one of the four approximate target directions. Thereafter, EMG signals were decoded to perform the grasping task using an attached gripper to the UR-10 robot arm. The proposed system was tested offline on three different healthy subjects, and mean validation accuracy of 93.62% and 99.50% were obtained across the three subjects for EOG and EMG decoding, respectively. Furthermore, the system was successfully tested in real-time with one subject, and mean online accuracy of 91.66% and 100% were achieved for EOG and EMG decoding, respectively. Our results obtained by combining real-time decoding of EOG and EMG signals for robot control show overall the potential of this approach to develop powerful and less complex HRI systems. Overall, this work provides a proof-of-concept for successful real-time control of robot arms using EMG and EOG signals, paving the way for the development of more dexterous and human-controlled assistive devices.
UR - http://www.scopus.com/inward/record.url?scp=85092742065&partnerID=8YFLogxK
U2 - 10.1109/ICRA40945.2020.9197550
DO - 10.1109/ICRA40945.2020.9197550
M3 - Conference contribution
AN - SCOPUS:85092742065
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3812
EP - 3817
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Y2 - 31 May 2020 through 31 August 2020
ER -
