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An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People.

Salam Dhou1, Ahmad Alnabulsi1, A R Al-Ali1

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a smart cane using Internet of Things (IoT) sensors and machine learning to help visually impaired individuals navigate safely. The smart cane detects obstacles and alerts users and guardians in emergencies, enhancing independent mobility.

Keywords:
IoTmachine learningsensorssmartphonevisually impaired peoplewalking assistants

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Area of Science:

  • Computer Science
  • Assistive Technology
  • Internet of Things (IoT)

Background:

  • Visually impaired individuals face significant challenges in daily navigation and interaction.
  • The Internet of Things (IoT) offers potential solutions for smart city applications, including aiding those with visual impairments.
  • Independent mobility is crucial for the quality of life for visually impaired people.

Purpose of the Study:

  • To propose and develop a smart IoT-based mobile sensor unit for a cane to enhance independent movement for visually impaired individuals.
  • To integrate obstacle detection and emergency alert functionalities into an assistive device.
  • To provide a safety tracking system for visually impaired cane users and their guardians.

Main Methods:

  • Development of a smart cane prototype integrating a six-axis accelerometer/gyro, ultrasonic sensors, GPS, cameras, and a microcomputer.
  • Implementation of an embedded machine learning algorithm for real-time obstacle identification and user alarming.
  • Creation of a companion mobile application for guardian-based user tracking via Google Maps.

Main Results:

  • The smart cane successfully collects environmental and user movement data.
  • The embedded machine learning algorithm effectively identifies and alerts users to detected obstacles.
  • The system includes an emergency alert for falls and a tracking feature for enhanced user safety.

Conclusions:

  • The proposed smart cane system demonstrates a viable solution for improving independent mobility for the visually impaired.
  • Integration of IoT sensors and machine learning in assistive devices can significantly enhance user safety and autonomy.
  • The developed prototype validates the system's potential for real-world application in smart city environments.