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Updated: Jun 5, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry.

Benoit Caby1, Suzanne Kieffer, Marie de Saint Hubert

  • 1Université Catholique de Louvain, Louvain-la-Neuve, Belgium. benoit.caby@uclouvain.be

Biomedical Engineering Online
|January 20, 2011
PubMed
Summary

This study developed a simple, objective method using accelerometers to classify hospitalized elderly individuals as fallers or non-fallers. This technology aids in early fall risk assessment and intervention for improved patient care.

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Falls in the elderly pose significant health and psychological challenges.
  • Predicting falls is crucial due to population aging and increased life expectancy.
  • Objective gait analysis is needed to supplement subjective fall risk assessments.

Purpose of the Study:

  • To develop a method for analyzing gait using accelerometers.
  • To classify hospitalized elderly individuals into faller and non-faller groups.
  • To provide an objective, user-friendly tool for fall risk assessment.

Main Methods:

  • Recorded accelerations of limbs using an accelerometer network during clinical tests.
  • Extracted 67 features from accelerometric signals during a walking test.

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Last Updated: Jun 5, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults
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Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults

Published on: October 25, 2024

  • Employed feature selection and classification algorithms to differentiate fallers and non-fallers.

    • Main Results:

    • Several classification algorithms successfully discriminated between fallers and non-fallers.
    • A subset of features was identified as significantly different between the groups.
    • The developed tool provides objective gait information without specialized labs.

    Conclusions:

    • A novel method classifies hospitalized elderly individuals based on fall risk using accelerometric data.
    • This represents a first step towards a comprehensive fall risk assessment system.
    • The tool can facilitate timely interventions and monitor rehabilitation progress.