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Related Experiment Video

Updated: Jul 10, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Daily fall risk assessment for older adults using a balance sensor with machine learning.

Jiaming Chen1, Xin Ma1, Ke Han Zou1

  • 1Department of Data and System Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.

Frontiers in Public Health
|July 9, 2026
PubMed
Summary

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A new 30-second balance sensor test using machine learning shows promise for assessing fall risk in older adults. This rapid method could improve fall prevention strategies by offering a practical alternative to lengthy traditional tests.

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Fall-risk assessment is crucial for preventing falls in older adults.
  • Conventional balance tests are often time-consuming and impractical for frequent clinical use.
  • There is a need for rapid, objective, and easily implementable fall-risk assessment tools.

Purpose of the Study:

  • To evaluate a 30-second quiet-standing assessment using a balance sensor and machine learning.
  • To classify fall history and assess fall risk in older adults.
  • To determine the feasibility of this method for frequent, real-world use.

Main Methods:

  • 394 older adults participated across laboratory, hospital, and home-based cohorts.
  • A balance sensor captured plantar pressure data during quiet standing.
Keywords:
balance sensorcenter of gravitycenter of pressurefall assessmentmachine learningolder adults

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Last Updated: Jul 10, 2026

Design and Analysis for Fall Detection System Simplification
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Published on: April 6, 2020

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  • Machine learning models analyzed center-of-pressure (CoP) and center-of-gravity (CoG) features for fall-risk classification.

    • Main Results:

    • The random forest model achieved an AUC of 0.71 (eyes-open) in the laboratory cohort.
    • External validation in the hospital cohort yielded an AUC of 0.68 and 72.0% accuracy.
    • The home-based study demonstrated feasibility for repeated, short-duration balance measurements.

    Conclusions:

    • A brief sensor-based standing assessment with machine learning offers a practical approach for fall-risk assessment in older adults.
    • This method shows potential as an improvement over conventional functional assessments.
    • Further prospective, multi-center studies are required to confirm predictive validity and clinical utility.