Multi-scale and multi-level feature assessment framework for classification of Parkinson’s disease state from short-term motor tasks

Peng, X., Zhao, Y., Li, Z. et al. (5 more authors) (2025) Multi-scale and multi-level feature assessment framework for classification of Parkinson’s disease state from short-term motor tasks. IEEE Transactions on Biomedical Engineering. ISSN 0018-9294

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2025 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Biomedical Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: decomposition; disease recognition; feature fusion; healthcare wearables system; machine learning; multi-dimensional; parkinson’s disease
Dates:
  • Published: 18 February 2025
  • Published (online): 18 February 2025
  • Accepted: 11 June 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
EPSRC/Industrial
165332
Depositing User: Symplectic Sheffield
Date Deposited: 20 Jun 2024 15:37
Last Modified: 03 Mar 2025 15:35
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TBME.2024.3418688
Open Archives Initiative ID (OAI ID):

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