Zhao, Y., Wang, X., Peng, X. et al. (8 more authors) (2024) Selecting and evaluating key MDS-UPDRS activities using wearable devices for Parkinson’s disease self-assessment. IEEE Journal of Selected Areas in Sensors, 1. pp. 177-189. ISSN 2836-2071
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices (ISWDs) such as mobile phones and smartwatches have been widely employed to analyse the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this paper, a novel technical pipeline is proposed for finegrained classification of PD severity grades, which identify the most representative activities. We also propose a multiactivities combination scheme based on MDS-UPDRS. Utilizing this scheme, symptom-related and complementary activities are captured. We collected 85 PD subjects of different severity grades using a single wrist sensor. Our best results demonstrate F1 scores of 95.75% for PD diagnosis and the fine-grained classification accuracy of PD disease grade is 82.41% when combing 4 activities which improved by 11.02% over a single activity. The experiments and theoretical analyses can serve as a useful foundation for future investigations into the effect of proposed solutions for PD diagnosis in uncontrolled environment setup, ultimately leading to self-PD assessment in the home environment.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Motors; Sensors; Diseases; Wearable devices; Medical diagnostic imaging; Biomarkers; Pipelines |
Dates: |
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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: | 25 Jul 2024 10:16 |
Last Modified: | 20 Nov 2024 16:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/JSAS.2024.3432714 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214875 |