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Related Experiment Video

Updated: May 24, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Development of A Wearable Device for Fall Monitoring: A Preliminary Study.

Supitchapong Tanakietpinyo1, Le Ke Nghiep2, Anuwat Khotprom3

  • 1Somdej Phra Sangkharat Yanasangwon Geriatric Hospital, Department of Medical Services, Ministry of Public Health, Thailand.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Older adults experiencing falls can be identified by increased postural sway. This study quantifies sway during daily activities to train an AI fall detection system, linking sway to slower gait speed.

Keywords:
Fall preventionMachine learningMobile applicationWearable device

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Public Health

Background:

  • Falls are a major public health concern for older adults, affecting 30% annually.
  • Developing effective fall detection systems is crucial for preventing injuries and improving quality of life.

Purpose of the Study:

  • To quantify postural sway in older adults during standardized activities of daily living (ADLs).
  • To create a dataset for training an Artificial Intelligence (AI) model for fall risk assessment.
  • To identify high-risk motion patterns associated with falls in older adults.

Main Methods:

  • Utilized a wrist-worn 6-axis motion sensor to collect data from 50 older adults.
  • Measured postural sway (displacement) during 16 standardized ADLs.
  • Compared movement displacements between slow-walking and normal-walking groups.

Main Results:

  • Quantified postural sway and movement displacements during various ADLs.
  • The slow-walking group (4.34±1.01 s 10MWT) showed significantly greater movement displacements than the normal-walking group (3.48±0.71 s).
  • Established a direct association between increased postural sway and slower gait speed.

Conclusions:

  • Increased postural sway is a validated indicator of fall risk in older adults.
  • The developed dataset and AI model show promise for a novel fall detection system.
  • Gait speed is a key factor correlated with postural sway and fall risk.