Sheng, B, Xie, S, Tang, L et al. (2 more authors) (2019) An Industrial Robot-Based Rehabilitation System for Bilateral Exercises. IEEE Access, 7. pp. 151282-151294. ISSN 2169-3536
Abstract
Robot-assisted rehabilitation devices can provide intensive and precise task-based training that differs from clinician-facilitated manual therapy. However, industrial robots are still rarely used in rehabilitation, especially in bilateral exercises. The main purpose of this research is to develop and evaluate the functionality of a bilateral upper-limb rehabilitation system based on two modern industrial robots. A `patient-cooperative' control strategy is developed based on an adaptive admittance controller, which can take into account patients' voluntary efforts. Three bilateral training protocols (passive, active, and self) are also proposed based on the system and the control strategy. Experimental results from 10 healthy subjects show that the proposed system can provide reliable bilateral exercises: the mean RMS values for the master error and the master-slave error are all less than 1.00 mm and 1.15 mm respectively, and the mean max absolute values for the master error and the master-slave error are no greater than 6.11 mm and 6.73 mm respectively. Meanwhile, the experimental results also confirm that the recalculated desired trajectory can present the voluntary efforts of subjects. These experimental findings suggest that industrial robots can be used in bilateral rehabilitation training, and also highlight the potential applications of the proposed system in further clinical practices.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright, all rights reserved. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Service robots , Trajectory , Robot kinematics , Admittance , Training , Real-time systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) GCRF_IS_2017 |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jun 2020 11:00 |
Last Modified: | 29 Jun 2020 11:00 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/access.2019.2948162 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162395 |
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