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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Movement Disorders
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1472956
This article is part of the Research Topic Digital biomarkers in movement disorders View all 11 articles

Gait analysis in the early stage of Parkinson's disease with a machine learning approach

Provisionally accepted
Yin Wenchao Yin Wenchao 1Zhu Wencheng Zhu Wencheng 2*Gao Hong Gao Hong 3*Niu Xiaohui Niu Xiaohui 1*Shen Chenxin Shen Chenxin 1*Xiangmin Fan Xiangmin Fan 4Wang Cui Wang Cui 1*
  • 1 Central Hospital of Dalian University of Technology, Dalian, China
  • 2 Beijing CAS-Ruiyi Information Technology Co., Ltd., Beijing, China
  • 3 Dalian Maritime University, Dalian, Liaoning Province, China
  • 4 Chinese Academy of Sciences (CAS), Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    Background Gait disorder is a prominent motor symptom in Parkinson's disease (PD), objective and quantitative assessment of gait is essential for diagnosing and treating PD, particularly in its early stage.This study utilized a non-contact gait assessment system to investigate gait characteristics between individuals with PD and healthy controls, with a focus on early-stage PD. Additionally, we trained two machine learning models to differentiate early-stage PD patients from controls and to predict MDS-UPDRS III score.Early-stage PD patients demonstrated reduced stride length, decreased gait speed, slower stride and swing speeds, extended turning time, and reduced cadence compared to controls. Our model, after an integrated analysis of gait parameters, accurately identified early-stage PD patients. Moreover, the model indicated that gait parameters could predict the MDS-UPDRS III score using a machine learning regression approach.The non-contact gait assessment system facilitates the objective and quantitative evaluation of gait disorder in PD patients, effectively distinguishing those in the early stage from healthy individuals. The system holds significant potential for the early detection of PD. It also harnesses gait parameters for a reasoned prediction of the MDS-UPDRS III score, thereby quantifying disease severity. Overall, gait assessment is a valuable method for the early identification and ongoing monitoring of PD.

    Keywords: Parkinson's disease, gait analysis, Early-stage diagnosis, MDS-UPDRS III score, Non-contact assessment, machine learning

    Received: 30 Jul 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Wenchao, Wencheng, Hong, Xiaohui, Chenxin, Fan and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Zhu Wencheng, Beijing CAS-Ruiyi Information Technology Co., Ltd., Beijing, China
    Gao Hong, Dalian Maritime University, Dalian, 116026, Liaoning Province, China
    Niu Xiaohui, Central Hospital of Dalian University of Technology, Dalian, China
    Shen Chenxin, Central Hospital of Dalian University of Technology, Dalian, China
    Wang Cui, Central Hospital of Dalian University of Technology, Dalian, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.