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Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor.

Yoosuf Nizam1,2, Mohd Norzali Haji Mohd3,4, M Mahadi Abdul Jamil5

  • 1Biomedical Engineering Modeling and Simulation (BIOMEMS) Research Group, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia. he140090@siswa.uthm.edu.my.

Sensors (Basel, Switzerland)
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Summary

This study introduces an improved fall detection system using depth sensors. It personalizes fall risk assessment, enhancing detection accuracy for diverse individuals, especially the elderly.

Keywords:
assistive livingdaily activitiesfall risk levelfallshuman fall

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Computer Science

Background:

  • Unintentional falls pose a significant public health challenge, particularly for aging populations.
  • Existing fall detection methods utilize various sensors and approaches, including wearables and cameras.
  • Accurate fall detection systems are crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop an adaptive fall detection algorithm using depth sensor technology.
  • To enhance fall detection accuracy by incorporating individual fall risk level identification.
  • To create a personalized monitoring system that accounts for varying physical strengths.

Main Methods:

  • Utilized a depth sensor for human activity classification and fall detection.
  • Developed a unique procedure to identify individual fall risk levels.
  • Adapted the fall detection algorithm based on identified fall risk levels to personalize its performance.

Main Results:

  • The proposed approach demonstrated improved accuracy in fall detection.
  • The system effectively adapted to individuals with different fall risk levels.
  • Experimental results showed promising performance in personalized fall detection.

Conclusions:

  • Integrating fall risk level identification significantly enhances fall detection accuracy.
  • Depth sensor-based adaptive algorithms offer a promising solution for personalized fall monitoring.
  • This approach holds potential for improving safety and care for vulnerable populations.