Document Type
Article
Publication Date
2024
DOI
10.1109/JSAS.2024.3432714
Publication Title
IEEE Journal of Selected Areas in Sensors
Volume
Article in Press
Pages
1-13
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 fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multi-activities 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.
Rights
© 2024 The Authors.
This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
ORCID
0000-0002-3263-5217 (Xu)
Original Publication Citation
Zhao, Y., Wang, X., Peng, X., Li, Z., Nan, F., Zhou, M., Qi, J., Yang, Y., Zhao, Z., Xu, L., & Yang, P. (2024). Selecting and evaluating key MDS-UPDRS activities using wearable devices for Parkinson's disease self-assessment. IEEE Journal of Selected Areas in Sensors. Advance online publication. https://doi.org/10.1109/JSAS.2024.3432714
Repository Citation
Zhao, Yuting; Wang, Xulong; Peng, Xiyang; Li, Ziheng; Nan, Fengtao; Zhuo, Menghui; Qi, Jun; Yang, Yun; Zhao, Zhong; Xu, Lida; and Yang, Po, "Selecting and Evaluating Key MDS-UPDRS Activities Using Wearable Devices for Parkinson's Disease Self-Assessment" (2024). Information Technology & Decision Sciences Faculty Publications. 100.
https://digitalcommons.odu.edu/itds_facpubs/100
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Artificial Intelligence and Robotics Commons, Nervous System Diseases Commons