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Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial

Wei-Chih Lien1,2,3, Congo Tak-Shing Ching3,4, Zheng-Wei Lai5

  • 1Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

Frontiers in Bioengineering and Biotechnology
|June 1, 2022
PubMed
Summary
This summary is machine-generated.

The k-nearest neighbor (kNN) algorithm effectively uses gait stability and symmetry from ankle accelerometry to assess fall risk in older adults. This method accurately distinguishes between fallers and non-fallers, identifying key gait features for risk assessment.

Keywords:
accelerometryfall riskgaitolder adultsstabilitysymmetry

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Clinical Biomechanics

Background:

  • Falls are a significant health concern for older adults, leading to injury and reduced quality of life.
  • Accurate fall risk assessment is crucial for implementing timely interventions.
  • Gait analysis using wearable sensors offers a promising, non-invasive approach to evaluate fall risk.

Purpose of the Study:

  • To develop and validate a fall risk assessment tool using the k-nearest neighbor (kNN) algorithm.
  • To investigate the efficacy of gait stability and symmetry metrics derived from accelerometry in classifying fall risk.
  • To identify specific gait features that can effectively distinguish between older adults who are fallers and non-fallers.

Main Methods:

  • Participants included 15 non-fallers and 12 recurrent fallers without significant neurological or musculoskeletal conditions.
  • Gait data was collected using tri-axial accelerometry during a 10-meter walking test (10MWT).
  • Normalized cross-correlation (NCC) coefficients for gait stability (unilateral) and symmetry (bilateral) were calculated, and the kNN algorithm was applied for classification.

Main Results:

  • Recurrent fallers exhibited longer 10MWT durations compared to non-fallers (p < 0.05).
  • kNN classification achieved high F1 scores: 92% for non-fallers and 89% for recurrent fallers using x-axis acceleration.
  • Root sum of squares (RSS) of gait signals yielded excellent classification accuracy: 95% for non-fallers and 94% for recurrent fallers.

Conclusions:

  • The kNN algorithm, combined with gait stability and symmetry features from accelerometry, demonstrates high accuracy in fall risk assessment.
  • Root sum of squares (RSS)-based normalized cross-correlation (NCC) coefficients are effective gait features for identifying individuals at risk of falls.
  • This approach provides a valuable tool for objective and reliable fall risk stratification in older adults.