Hartwell, A., Kadirkamanathan, V. orcid.org/0000-0002-4243-2501 and Anderson, S. (2016) Person-specific gesture set selection for optimised movement classification from EMG signals. In: 2016 IEEE 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), 16-20 Aug 2016, Orlando, Florida. Institute of Electrical and Electronics Engineers , pp. 880-883. ISBN 9781457702204
Abstract
© 2016 IEEE.Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different sets of movements that would lead to high classification accuracy. The novel approach we take here is to instead use a data-driven diagnostic test to select a set of person-specific movements. This new approach leads to an optimised set of movements for a specific person with regards to classification performance.
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 users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Electromyography; Training; Support vector machines; Kernel; Testing; Prosthetics; Databases |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Feb 2017 14:50 |
Last Modified: | 19 Dec 2022 13:35 |
Published Version: | https://doi.org/10.1109/EMBC.2016.7590841 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/EMBC.2016.7590841 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:111294 |