TY - GEN
T1 - A Teleoperated Manipulator Control System Based on Surface Electromyography Using Deep Learning
AU - Lai, Zhiping
AU - Zhang, Xueze
AU - Wang, Junkongshuai
AU - Mu, Wei
AU - Wang, Aiping
AU - Niu, Lan
AU - Zhang, Lihua
AU - Wang, Hongbo
AU - Kang, Xiaoyang
AU - Bin, Jianxiong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Gesture recognition based on surface electromyography (sEMG) is a popular topic in teleoperation field, which has the characteristics of convenience, maneuverability, flexibility and adaptability. With the continuous development of deep learning technology, the variety and accuracy of gesture recognition are constantly improving. In our previous research, a new neural network based on graph convolutional network and temporal convolutional network (STCN-GR) was proposed and achieved good results in gesture recognition task. In this paper, we develop a teleoperated manipulator control system based on STCN-GR combined with majority voting. The experimental results show that the performance of proposed system can achieve relatively excellent performance in recognition accuracy and computation speed. In this paper, we choose a mechanism hand as a peripheral to test the performance of the STCN-GR based control system, and the results show that this system can be applied in teleoperation in feature work to provide convenience for human life.
AB - Gesture recognition based on surface electromyography (sEMG) is a popular topic in teleoperation field, which has the characteristics of convenience, maneuverability, flexibility and adaptability. With the continuous development of deep learning technology, the variety and accuracy of gesture recognition are constantly improving. In our previous research, a new neural network based on graph convolutional network and temporal convolutional network (STCN-GR) was proposed and achieved good results in gesture recognition task. In this paper, we develop a teleoperated manipulator control system based on STCN-GR combined with majority voting. The experimental results show that the performance of proposed system can achieve relatively excellent performance in recognition accuracy and computation speed. In this paper, we choose a mechanism hand as a peripheral to test the performance of the STCN-GR based control system, and the results show that this system can be applied in teleoperation in feature work to provide convenience for human life.
UR - http://www.scopus.com/inward/record.url?scp=85141208476&partnerID=8YFLogxK
U2 - 10.1109/CYBER55403.2022.9907033
DO - 10.1109/CYBER55403.2022.9907033
M3 - Conference contribution
AN - SCOPUS:85141208476
T3 - 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022
SP - 1281
EP - 1286
BT - 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022
Y2 - 27 July 2022 through 31 July 2022
ER -