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Enhancing gesture recognition for assisting visually impaired persons using deep learning in an IoT environment-based

Hanan Abdullah Mengash1, Basma S Alqadi2, Radwa Marzouk3,4

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia. hamengash@pnu.edu.sa.

Scientific Reports
|November 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced gesture recognition system using deep learning and an improved snake optimization algorithm to aid visually impaired individuals. The novel approach achieves 98.62% accuracy, significantly improving real-time gesture interpretation.

Keywords:
Deep learningGesture recognitionInternet of ThingsSnake optimisation algorithmVisually impaired people

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Gesture recognition (GR) is crucial for interfaces but faces challenges for visually impaired users.
  • Conventional machine learning (ML) struggles with real-time performance, necessitating advanced solutions.
  • Deep learning (DL) offers superior capabilities for complex pattern recognition in GR.

Purpose of the Study:

  • To develop an enhanced gesture recognition system (EGRVI-DLISOA) for the visually impaired in an IoT environment.
  • To leverage deep learning and an improved snake optimization algorithm for accurate real-time gesture interpretation.
  • To address the challenges faced by visually impaired individuals in daily tasks and technology interaction.

Main Methods:

  • Utilized the Sobel filter (SF) for noise elimination in gesture data.
  • Employed the SqueezeNet model for efficient feature extraction from visual data.
  • Implemented Long Short-Term Memory (LSTM) for gesture classification, optimized by an improved snake optimization algorithm (ISOA).

Main Results:

  • The EGRVI-DLISOA technique demonstrated superior performance in gesture recognition.
  • Achieved a high accuracy rate of 98.62% on the hand gestures dataset.
  • Outperformed existing gesture recognition models in experimental evaluations.

Conclusions:

  • The EGRVI-DLISOA approach significantly enhances gesture recognition for visually impaired users.
  • Deep learning combined with optimization algorithms offers a powerful solution for real-time assistive technologies.
  • This system provides a promising advancement in computer interfaces for individuals with visual impairments.