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Yassine Rabhi1, Makrem Mrabet1, Farhat Fnaiech1

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This study introduces a low-cost hand gesture-controlled wheelchair (EWC) system, enabling mobility for patients unable to use joysticks. The intelligent system uses AI-powered visual recognition for real-time control with variable speed capabilities.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Rehabilitation Technology

Background:

  • Standard electric wheelchairs often require joystick manipulation, posing challenges for individuals with limited hand function.
  • There is a need for intuitive and accessible control systems for patients with diverse physical disabilities.

Purpose of the Study:

  • To develop and validate a novel hand gesture-controlled wheelchair (EWC) system.
  • To provide an alternative mobility solution for patients unable to operate traditional wheelchairs.
  • To enable real-time, variable-speed control of a wheelchair using hand gestures.

Main Methods:

  • A camera-based system utilizing visual recognition algorithms and artificial intelligence software was implemented.
  • A 3D environment simulator was used for initial safety and performance testing.
  • The system was tested on real patients with various hand pathologies in a clinical setting.

Main Results:

  • The hand gesture-controlled wheelchair (EWC) demonstrated successful real-time control based on recognized hand movements.
  • The system allows for variable speed control, mimicking the functionality of a standard joystick.
  • Low-cost implementation and positive results were observed during testing on patients.

Conclusions:

  • The developed hand gesture-controlled wheelchair (EWC) system is a viable and effective solution for enhancing mobility in patients with hand impairments.
  • The intelligent control system proved valid and reliable in clinical trials with diverse patient groups.
  • This technology offers a promising, low-cost alternative for assistive mobility devices.