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Author:

Li, Jianfeng (Li, Jianfeng.) (Scholars:李剑锋) | Fan, Wenpei (Fan, Wenpei.) | Dong, Mingjie (Dong, Mingjie.) | Rong, Xi (Rong, Xi.)

Indexed by:

EI Scopus

Abstract:

For patients with ankle injuries, rehabilitation training is an important and effective way to help patients restore their ankle complex's motor abilities. Aiming to improve the accuracy and performance of ankle rehabilitation, the authors focus on the control strategies of the developed parallel ankle rehabilitation robot with novel 2-UPS/RRR mechanism. Firstly, the kinematics model of the mechanism is established, and they deduce the inverse solution of positions as well as the velocity mapping between the driving speed and the robot's angular velocity, based on which they realise the trajectory tracking control in the process of passive rehabilitation training. Secondly, they set up experiments to determine the torque threshold that can be used to detect the motion intention of ankle joint, and then they propose the active rehabilitation training strategy according to the motion intention detection. Finally, experiments were carried out with healthy subjects, with results showing that the trajectory tracking error during passive rehabilitation training is very small, and the moving platform of the ankle rehabilitation robot can drive the ankle joint to the detected motion intention direction at a constant speed flexibly and smoothly, which verifies the effectiveness of the control strategies for ankle rehabilitation training. Copyright © 2020 Cognitive Computation and Systems. All rights reserved.

Keyword:

Mechanisms Educational robots Patient rehabilitation Robots Motion tracking

Author Community:

  • [ 1 ] [Li, Jianfeng]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Fan, Wenpei]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Dong, Mingjie]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Rong, Xi]Department of Neurology, Affiliated Hospital of Qingdao University, Qingdao; 266000, China

Reprint Author's Address:

  • [dong, mingjie]college of mechanical engineering and applied electronics technology, beijing university of technology, beijing; 100124, china

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Source :

Cognitive Computation and Systems

Year: 2020

Issue: 3

Volume: 2

Page: 105-111

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 25

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 10

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