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Machine learning classification method for wheelchair detection using bag-of-visual-words technique.

Hamid A Jalab1, Ahmad Sami Al-Shamayleh2, Mosleh M Abualhaj3

  • 1Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi Qar, Iraq.

Disability and Rehabilitation. Assistive Technology
|March 11, 2025
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Summary
This summary is machine-generated.

This study introduces a machine learning model for automatic wheelchair detection using visual surveillance, achieving 98.85% accuracy. This advances smart healthcare and assistive technology for improved mobility and safety.

Keywords:
Wheelchair detectionbag of featuresfeature extractionmobility aidssupport vector machine

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Rehabilitation Engineering

Background:

  • Wheelchair users require enhanced safety and accessibility in smart healthcare environments.
  • Autonomous navigation and mobility support systems can significantly benefit from accurate wheelchair detection.

Purpose of the Study:

  • To develop an automatic wheelchair detection system using visual surveillance.
  • To improve safety and accessibility for wheelchair users through enhanced mobility support.

Main Methods:

  • A novel machine learning model utilizing the bag-of-visual-words (BoVWs) technique was developed.
  • Key feature extraction, visual vocabulary construction, and histogram-based image representation were employed.
  • A support vector machine (SVM) classifier was used for image classification.

Main Results:

  • The proposed method achieved a high accuracy of 98.85% in wheelchair detection.
  • The model demonstrated effectiveness in identifying wheelchairs within images.

Conclusions:

  • Object detection techniques show significant potential for recognizing mobility aids.
  • This technology can contribute to improved accessibility and safety in assistive technology applications.