Cover Image
QR Code

Classification of hemiplegia through gait analysis and machine learning methods.

Objective: Gait analysis is a method that is used for understanding normal walking and determining the stage of the disease as it affects walking. It is important to objectively determine the stage of the disease in order to decide interventions and treatment strategies. This study aims to determine...

Full description

Published in:Marmara Medical Journal Vol. 37; no. 1; pp. 5 - 11
Main Authors: TAS, Hazal, YARDIMCI, Ahmet, UYSAL, Hilmi, BILGE, Ugur
Format: research tables/charts Journal Article
Published: Marmara Medical Journal Jan2024
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177058272&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 177058272
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        10191941
        787L
      jtl: Marmara Medical Journal
      issn: 10191941
      maglogo: N
    pubinfo:
      dt: Jan2024
      vid: 37
      iid: 1
      pid: 47510
      pub: Marmara Medical Journal
    artinfo:
      ui:
        177058272
        177058272
        177058272
        10.5472/marumj.1379890
        177058272
      ppf: 5
      ppct: 6
      formats:
      tig:
        atl: Classification of hemiplegia through gait analysis and machine learning methods.
      aug:
        au:
          TAS, Hazal
          YARDIMCI, Ahmet
          UYSAL, Hilmi
          BILGE, Ugur
        affil: Department of Biostatistics and Medical Informatics, Faculty of Medicine, Akdeniz University, Antalya, Turkey
      sug:
        subj:
          Hemiplegia Classification
          Machine Learning Methods
          Gait Analysis
          Human
          Accelerometers
          Data Analysis Software
          Algorithms
      ab: Objective: Gait analysis is a method that is used for understanding normal walking and determining the stage of the disease as it affects walking. It is important to objectively determine the stage of the disease in order to decide interventions and treatment strategies. This study aims to determine the Brunnstrom Stage of the hemiplegic patients with an analysis of gait data. Patients and Methods: In the first part of the study, the gait signal data were taken from 28 post-stroke hemiplegic patients and 7 healthy individuals with three-axis accelerometers. In the second part, new gait data were collected from 15 healthy individuals through an accelerometer on the anteroposterior axis. First the accelerometer signals were decomposed to Daubechies 5 (Db5) level six wavelets using MATLAB software. Subsequently, these attributes were classified through several classifier and machine learning algorithms on WEKA and MATLAB software packages to predict the stages of hemiplegia. Results: The highest accuracy rate in the prediction of hemiplegia stage was achieved with the LogitBoost algorithm on WEKA with 91% for 35 samples, and 90% for 50 samples. This performance was followed by the RUSBoosted Trees algorithm on the MATLAB software with an accuracy of 86.1% correct prediction. Conclusion: The Brunnstrom Stage of hemiplegia can be predicted with machine learning algorithms with a good accuracy, helping physicians to classify hemiplegic patients into correct stages, monitor and manage their rehabilitation.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N