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Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System.

Barbara Pȩkala1,2, Teresa Mroczek2, Dorota Gil2

  • 1Institute of Computer Science, University of Rzeszów, 35-310 Rzeszów, Poland.

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|February 26, 2022
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Summary
This summary is machine-generated.

This study enhances fall detection for aging-at-home technology by improving posture recognition from depth maps. A new hybrid system effectively identifies lying poses, crucial for fall detection systems.

Keywords:
aggregation functionfuzzy inferenceknowledge measureposture detectionprecedence indicatorrule induction

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Area of Science:

  • Gerontology
  • Computer Science
  • Biomedical Engineering

Background:

  • The global population is aging rapidly, increasing the need for effective aging-at-home technologies.
  • Reliable and unobtrusive human activity monitoring is essential for elder care and fall prevention.
  • Fall detection systems are critical for ensuring the safety of elderly individuals living independently.

Purpose of the Study:

  • To improve posture detection, a key component of fall detection systems for aging-at-home technology.
  • To develop and evaluate a novel hybrid system for accurate lying pose detection using depth map data.
  • To investigate the impact of different rule sets and inference aggregation approaches on system performance.

Main Methods:

  • A two-stage fall detection method utilizing depth maps from a Microsoft Kinect sensor.
  • Focus on the first stage: pose recognition from depth maps, specifically for detecting lying poses.
  • Development of a hybrid FRSystem incorporating rule sets derived from domain knowledge and rough set theory.
  • Evaluation of two inference aggregation approaches, with and without a proposed axiomatic knowledge measure.

Main Results:

  • The proposed hybrid FRSystem demonstrates effectiveness in lying pose detection.
  • The new axiomatic definition of knowledge measures positively impacts inference effectiveness.
  • Rule induction methods successfully reduce the number of rules while maintaining system performance.
  • The research validates the importance of accurate posture recognition in fall detection.

Conclusions:

  • The developed hybrid FRSystem offers a promising solution for improving posture detection in fall detection systems.
  • The integration of axiomatic knowledge measures enhances the inference capabilities of the system.
  • Rough set theory-based rule induction provides an efficient way to manage rule sets for posture recognition.
  • This research contributes to the advancement of aging-at-home technologies for enhanced elder safety.