Abstract
In the present work, tele-manipulation of robot arm and gripper is experimentally performed using inertia measurement unit (IMU) and electromyogram (EMG)-based human motion recognition. The movement of robot arm and motion of robot gripper is determined based on the measured IMU and EMG data, respectively. To overcome user dependence which is one of main disadvantage of EMG-based motion recognition, reference voluntary contraction method-based normalization of measured EMG data is carried out. Training and test data of EMG are obtained from experiments for four kinds of hand motion of four experimental participants. After extraction of feature vectors, artificial neural network is applied for the EMG-based hand motion recognition. Even when training data and test data are obtained from different participants, it is confirmed that classification accuracy can be greatly improved through the proposed simple normalization method. Finally, a real-time tele-manipulation of 6-degree-of-freedom robot arm is demonstrated successfully by adopting the proposed user independent human motion recognition method.
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Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A307 4547). This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2022-2020-0-01612) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).
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Jung Woo Sohn is a Professor of the Department of Mechanical Design Engineering of Kumoh National Institute of Technology in Korea. He received his Ph.D. in Mechanical Engineering from Inha University in Korea. His research interests include smart materials, design and control of smart structures, smart system for vehicle applications, human-machine interaction and prognostics and health management (PHM).
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Yuk, DG., Sohn, J.W. User independent hand motion recognition for robot arm manipulation. J Mech Sci Technol 36, 2739–2747 (2022). https://doi.org/10.1007/s12206-022-0507-x
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DOI: https://doi.org/10.1007/s12206-022-0507-x