Accurate fall risk classification in elderly using one gait cycle data and machine learning.

Journal: Clinical biomechanics (Bristol, Avon)
PMID: 38744224

Abstract

BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. This challenge stems from individual variability and step-to-step fluctuations, making accurate classification difficult.

Authors

  • Daisuke Nishiyama
    Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan. Electronic address: dnishiya@wakayama-med.ac.jp.
  • Satoshi Arita
    Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan.
  • Daisuke Fukui
    Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan.
  • Manabu Yamanaka
    Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan, 811-1 Kimiidera, Wakayama 641-0012, Japan.
  • Hiroshi Yamada
    Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Health and Nutrition.

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