Zeng, X, Zhu, G, Li, P et al. (3 more authors) (2018) A Feasibility Study of Robot-Assisted Ankle Training Triggered by Combination of SSVEP Recognition and Motion Characteristics. In: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE/ASME AIM 2018, 09-12 Jul 2018, Auckland, New Zealand. IEEE , pp. 1246-1251. ISBN 978-1-5386-1854-7
Abstract
In order to inspire subjects exerting more energy and pay more attention to SSVEP-based ankle training, this study introduce motion intention detection both in the first half cycle of single trainings and at the beginning of the training. This study also propose a novel method to recognize motion intention of subjects through merging the motion characteristics of the ankle training into the identification of SSVEP signals. Five healthy subjects participate in the training, and all can accomplish the training with the success rate of more than 80%. The proposed hybrid method can increase success rate from 50% to 80% comparing with the identification of SSVEP signals.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 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 |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering IAPP1R2\100056 |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Oct 2018 10:11 |
Last Modified: | 26 Oct 2018 10:11 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/AIM.2018.8452390 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137779 |