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RETRACTED ARTICLE: Gesture recognition and response system for special education using computer vision and

Duan Xuanfeng^1,2

¹Sichuan Provincial Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education, Leshan Normal University, Leshan City, Sichuan Province, China.

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|July 8, 2025

Summary

This study introduces an advanced gesture recognition system for special education, achieving 95.4% accuracy. Model compression using genetic algorithms (GAs) enhances real-time application on mobile devices.

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Gesture recognition is crucial for improving human-computer interaction (HCI), particularly in special education.
Existing systems face challenges in deployment on resource-constrained devices.

Purpose of the Study:

To develop and evaluate a comprehensive gesture recognition and response system for special education applications.
To optimize the system for real-time performance on mobile and embedded platforms.

Main Methods:

Utilized deep learning architectures (AlexNet, VGG19, ResNet, MobileNet) and machine learning algorithms (SVM, Random Forest).
Employed genetic algorithms (GAs) for model compression to reduce size and inference time.
Evaluated system performance on a diverse gesture dataset under various conditions.

Main Results:

Achieved state-of-the-art accuracy of 95.4% in complex gesture recognition.
Reduced model size by 42% and inference time by 45% through GA-based optimization.
Demonstrated robustness across different lighting, user demographics, and physical variations.

Conclusions:

The developed gesture recognition system shows significant potential as an assistive tool in special education.
Model optimization is key for deploying advanced AI in real-world, resource-limited environments.
Future work includes expanding the gesture library and integrating multimodal inputs for enhanced adaptability.