Abass, Z, Meng, W orcid.org/0000-0003-0209-8753, Xie, SQ et al. (1 more author) (2019) A robust, practical upper limb electromyography interface using dry 3D printed electrodes. In: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 08-12 Jul 2019, Hong Kong. IEEE , pp. 453-458. ISBN 9781728124933
Abstract
This study aims to develop a practical, robust and reliable human-machine interface using gesture recognition based on surface electromyography (sEMG) signals from the forearm. This technology is developed to be employed medically in stroke rehabilitation or prosthetic control. So far, studies have been conducted that improved the accuracy of such systems, but little has been done to avoid using wet (gelled) electrodes and hence improve their reliability and robustness for long-term use. Through this study, a comfortable and wearable bio- signal acquisition device is designed and developed that uses dry EMG electrodes. 3D printed electrodes are compared with ready-made dry ones to choose the better option, and an interface is established that allows control of any mechatronic system such as a prosthetic arm.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2019 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. |
Keywords: | —EMG interface, 3D-printed electrodes, neuromuscular interface, wearable |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Dec 2019 12:17 |
Last Modified: | 04 Dec 2019 12:17 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/AIM.2019.8868500 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154170 |