Predicting UPDRS Motor Symptoms in Individuals With Parkinson's Disease From Force Plates Using Machine Learning-Reference-Cited by-同舟云学术

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Predicting UPDRS Motor Symptoms in Individuals With Parkinson's Disease From Force Plates Using Machine Learning

Published:2022-07 Issue:7 Volume:26 Page:3486-3494
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Exley Trevor¹^ORCID,Moudy Sarah²,Patterson Rita M.²^ORCID,Kim Joonghyun³^ORCID,Albert Mark V.⁴^ORCID

Affiliation:

1. Department of Biomedical Engineering, University of North Texas, Denton, TX, USA

2. Department of Family and Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA

3. Texas Academy of Mathematics and Science, University of North Texas, Denton, TX, USA

4. Department of Computer Science, University of North Texas, Denton, TX, USA

Funder

American Osteopathic Association

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9813470/09730065.pdf?arnumber=9730065

Reference40 articles.

1. Use of Nintendo Wii Balance Board for posturographic analysis of Multiple Sclerosis patients with minimal balance impairment

2. Reliability and validity of the Wii Balance Board for assessment of standing balance: A systematic review

3. Proprioception and Motor Control in Parkinson's Disease

4. Parkinson’s Disease Rigidity: Relation to Brain Connectivity and Motor Performance

5. Quantification of Head Movement Predictability and Implications for Suppression of Vestibular Input during Locomotion

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1. Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements;Ageing Research Reviews;2024-08

2. PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment;Heliyon;2024-07

3. Parkinson's Disease Severity Assessment: A Comparative Study & Interpretability Analysis;2024 5th International Conference for Emerging Technology (INCET);2024-05-24

4. Machine learning approaches for neurological disease prediction: A systematic review;Expert Systems;2024-04-04

5. Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson’s Disease Using Biomedical Voice Measures;International Journal of Fuzzy Systems;2024-02-19

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