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Related Concept Videos

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Computer-Vision- and Edge-Enabled Real-Time Assistance Framework for Visually Impaired Persons with LPWAN Emergency

Ghadah Naif Alwakid1,2, Mamoona Humayun3, Zulfiqar Ahmad4

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced assistive framework for visually impaired individuals, enhancing mobility with real-time object detection and dual communication channels for reliable navigation and emergency alerts.

Keywords:
LPWANassistive technologycomputer visiondeep learningedge computinghealthcareobstacle detectionreal-time monitoringwireless communication

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Area of Science:

  • Assistive Technology
  • Computer Vision
  • Edge Computing

Background:

  • Existing assistive technologies for the visually impaired often lack real-time, universal capabilities in dynamic environments.
  • Current systems face trade-offs in range, latency, or reliability in varied indoor/outdoor settings.
  • A need exists for integrated solutions combining object detection, communication, and emergency signaling.

Purpose of the Study:

  • To propose a comprehensive assistive framework for visually impaired individuals.
  • To integrate computer vision, edge computing, and a dual-channel communication architecture.
  • To enhance independent mobility and quality of life through real-time assistance.

Main Methods:

  • Utilized the YOLOv5 deep-learning model for high-performance object detection (precision 0.988, recall 0.969, mAP 0.985).
  • Implemented edge computing for fast, local processing and decision-making, offloading central servers.
  • Designed a dual-channel communication system: Wi-Fi primary link and LoRaWAN (Low-Power Wide-Area Network) for emergency alerts.
  • Incorporated an IoT-based panic button for location-tagged emergency transmissions.

Main Results:

  • Demonstrated low latency and reliable system operation in diverse real-world conditions.
  • Achieved high accuracy in detecting obstacles, paths, and assistive tools.
  • The system proved effective in dynamic indoor and outdoor environments.
  • Validated the cost-effectiveness and scalability of the proposed architecture.

Conclusions:

  • The developed framework significantly enhances real-time assistance for the visually impaired.
  • The integration of YOLOv5, edge computing, and dual communication offers a robust solution.
  • The system has strong potential to improve independent mobility and safety for visually impaired users.
  • The architecture is suitable for deployment in complex environments requiring immediate support.