Zhu, G, Zeng, X, Zhang, M et al. (4 more authors) (2017) Robot-assisted ankle rehabilitation for the treatment of drop foot: A case study. In: Mechatronic and Embedded Systems and Applications (MESA), 2016 12th IEEE/ASME International Conference on. 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) 2016, 29-31 Aug 2016, Auckland, New Zealand. IEEE , pp. 1-5. ISBN 978-1-5090-6190-7
Abstract
This paper involves the use of an intrinsically-compliant ankle rehabilitation robot for the treatment of drop foot. The robot has a bio-inspired design by employing four Festo fluidic actuators that mimic skeletal muscles to actuate three rotational degrees of freedom (DOFs). A position controller in task space was developed to track the predefined trajectory of the end effector. The position tracking was achieved by the length tracking of each actuator in joint space by inverse kinematics. A stroke patient with drop foot participated in the trial as a case study to evaluate the potential of this robot for clinical applications. The patient gave positive feedback in using the ankle robot for the treatment of drop foot, although some limitations exist. The trajectory tracking showed satisfactory accuracy throughout the whole training with varying ranges of motion, with the root mean square deviation (RMSD) value being 0.0408 rad and the normalized root mean square deviation (NRMSD) value being 8.16%. To summarize, preliminary findings support the potential of the ankle rehabilitation robot for clinical applications. Future work will investigate the effectiveness of the robot for treating drop foot on a large sample of subjects.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jan 2018 09:54 |
Last Modified: | 11 Jan 2018 06:05 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MESA.2016.7587130 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125862 |