Miao, Q, Zhang, M, McDaid, A et al. (2 more authors) (2020) A robot-assisted bilateral upper limb training strategy with subject-specific workspace: A pilot study. Robotics and Autonomous Systems, 124. 103334. ISSN 0921-8890
Abstract
This paper proposes a new robot-assisted bilateral upper limb training strategy, focusing on the bilateral coordination of users’ upper limbs. The strategy is implemented and evaluated on a bilateral upper limb rehabilitation device (BULReD) that is an H-bot mechanism actuated by two Maxon DC motors. The control system consists of a position controller, an admittance controller and an adaptive algorithm, where the BULReD stiffness is modified session by session based on training performance. This strategy is also integrated with subject-specific workspace for enhanced training safety. Experiments were carried out with five subjects through active reaching tasks. Results indicate that the proposed training strategy requires significant coordination of bilateral upper limbs for task completion, and is able to tune control parameters to an appropriate difficulty level based on participants’ training performance. Future work will focus on its clinical evaluation on patients with upper limb disabilities.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Robotics and Autonomous Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Robot-assisted; Bilateral; Upper limb; Training strategy; ubject-specific workspace |
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 Academy of Medical Sciences GCRFNG\100213 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Feb 2020 16:37 |
Last Modified: | 22 Oct 2020 00:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.robot.2019.103334 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156490 |
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