Using supervised learning machine algorithm to identify future fallers based on gait patterns: A two-year longitudinal study.

Journal: Experimental gerontology
Published Date: Sep 11, 2019

Abstract

INTRODUCTION: Given their major health consequences in the elderly, identifying people at risk of fall is a major challenge faced by clinicians. A lot of studies have confirmed the relationships between gait parameters and falls incidence. However, accurate tools to predict individual risk among independent older adults without a history of falls are lacking.

Authors

  • Sophie Gillain
    Geriatric Department, Liège University Hospital, Route de Gaillarmont, 600, Chênée 4032, Belgium. Electronic address: sgillain@chuliege.be.
  • Mohamed Boutaayamou
    INTELSIG Laboratory, Department of Electrical Engineering and Computer Science, University of Liège, Belgium. Electronic address: mboutaayamou@uliege.be.
  • Cedric Schwartz
    Laboratory of Human Motion Analysis - LAMH, University of Liège, Belgium. Electronic address: cedric.schwartz@uliege.be.
  • Olivier Brüls
    Laboratory of Human Motion Analysis - LAMH, University of Liège, Belgium. Electronic address: o.bruls@uliege.be.
  • Olivier Bruyère
    World Health Organization Collaborating Center for Public Health Aspects of Musculoskeletal Health and Aging and Division of Public Health, Epidemiology and Health Economics, University of Liege, Belgium. Electronic address: Olivier.Bruyere@uliege.be.
  • Jean-Louis Croisier
    Laboratory of Human Motion Analysis - LAMH, University of Liège, Belgium; Science of Motricity Department, University of Liège, Belgium. Electronic address: jlcroisier@ulg.ac.be.
  • Eric Salmon
    Memory Clinic, Service of Neurology, CHU Liège, Liège, Belgium.
  • Jean-Yves Reginster
    Research Unit in Public Health, Epidemiology and Health Economics, University of Liege, Belgium; WHO Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Ageing, Chair for Biomarkers of Chronic Diseases, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi Arabia. Electronic address: jyreginster@uliege.be.
  • Gaëtan Garraux
    Neurology Department, University of Liège, Belgium; GIGA-CRC in vivo imaging, University of Liège, Belgium. Electronic address: ggarraux@uliege.be.
  • Jean Petermans
    Geriatric Department, CHU Liège, 600, Route de Gaillarmont, 4032 Chènée LIEGE, Belgium.

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