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Research on control method of upper limb exoskeleton based on mixed perception model

Published online by Cambridge University Press:  01 April 2022

WenDong Wang Open the ORCID record for WenDong Wang [Opens in a new window] ,
JunBo Zhang ,
Dezhi Kong Open the ORCID record for Dezhi Kong [Opens in a new window] ,
Shibin Su ,
XiaoQing Yuan  and
Chengzhi Zhao
WenDong Wang*
Affiliation:
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
JunBo Zhang
Affiliation:
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
Dezhi Kong
Affiliation:
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
Shibin Su
Affiliation:
Techinical Department, CSSC Guangzhou Huangpu Shipbuilding Company Limited, Guangzhou, China
XiaoQing Yuan
Affiliation:
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
Chengzhi Zhao
Affiliation:
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China
*
*Corresponding author. E-mail: wdwang@nwpu.edu.cn
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Abstract

As one of the research hotspots in the field of rehabilitation robotics, the upper limb exoskeleton robot has been widely used in the field of rehabilitation. However, the existing methods cannot comprehensively and accurately reflect the motion state of patients, which may lead to overtraining and secondary injury of patients in the process of rehabilitation training. In this paper, an upper limb exoskeleton control method based on mixed perception model of motion intention and intensity is proposed, which is based on the 6 degree-of-freedom upper limb rehabilitation exoskeleton in the laboratory. First, the kinematic information and heart rate information in the rehabilitation process of patients are collected, corresponding to patients’ motion intention and motion intensity, and fused to obtain the mixed perception vector. Second, the motion perception model based on long short-term memory neural network is established to realize the prediction of upper limb motion trajectory of patients and compared with back-propagation neural network to prove its effectiveness. Finally, the control system is built, and both offline and online test of the control method proposed are implemented. The experimental results show that the method can achieve comprehensive motion state perception of patients, realize real-time and accurate prediction trajectory according to human motion intention and intensity. The average prediction accuracy is 95.3%, and predicted joint angle error is less than 5 degrees. Therefore, the control method based on mixed perception model has good robustness and universality, which provides a new method for the active control of upper limb exoskeleton.

Keywords

exoskeletonmotion intentionmotion intensityupper limb rehabilitationLSTMtrajectory prediction
Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3669 - 3685
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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