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Design and Analysis for Fall Detection System Simplification
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Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Classification between non-multiple fallers and multiple fallers using a triaxial accelerometry-based system.

Ying Liu1, Stephen J Redmond, Michael R Narayanan

  • 1Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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This study developed a new method using a triaxial accelerometer (TA) to assess fall risk in older adults. The system accurately identifies individuals with multiple falls, aiding in fall prevention strategies.

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Falls are a significant health concern for older adults, leading to injuries and hospitalization.
  • Effective falls-risk assessment is crucial for developing targeted prevention strategies.

Purpose of the Study:

  • To develop and validate a classifier for discriminating between multiple fallers and non-multiple fallers in older adults.
  • To utilize data from a directed-routine (DR) movement test and a triaxial accelerometer (TA) for falls-risk assessment.

Main Methods:

  • 126 features were extracted from accelerometry signals recorded using a waist-mounted TA during DR tests from 68 subjects.
  • A linear multiple regression model estimated the number of previous falls.
  • A linear discriminant classifier was created using a threshold on the estimated falls to classify multiple vs. non-multiple fallers.

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Last Updated: May 25, 2026

Design and Analysis for Fall Detection System Simplification
08:05

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Published on: April 6, 2020

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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Main Results:

  • The system achieved 71% accuracy in classifying the exact number of falls in the preceding 12 months.
  • The classifier demonstrated high performance with 97% accuracy in identifying multiple fallers.

Conclusions:

  • A triaxial accelerometer-based classifier using DR movement tests shows promise for identifying older adults at higher risk of falls.
  • This technology can provide valuable information for personalized fall prevention interventions.