Asad A. Zaidi1,2, Sohaib Z. Khan1,2This email address is being protected from spambots. You need JavaScript enabled to view it., Halar Mustafa3, Ubaidillah2,4, and Toqeer Ali Syed5
1Mechanical Engineering Department, Faculty of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia
2King Salman Center For Disability Research (KSCDR), P.O. Box 94682, Riyadh 11614, Saudi Arabia
3Electrical Engineering Department, Faculty of Engineering Science and Technology, Hamdard University Karachi, Pakistan
4Mechanical Engineering Program, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami no. 36A, Kentingan, Surakarta, Central Java, Indonesia
5Faculty of Computer and Information Systems, Islamic University of Madinah, 42351 Al Madinah Al Munawwarah, Saudi Arabia
Received: May 7, 2025 Accepted: July 28, 2025 Publication Date: October 19, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Communication barriers remain a formidable obstacle for individuals with speech and motor impairments, particularly in resource-constrained settings where advanced assistive technologies are scarce. This study presents a dual-mode, cost-effective smart glove engineered to translate hand gestures into audible speech, empowering non-verbal users in developing regions. The first module employs a lightweight, logic-driven strategy for recognizing basic gestures using a single high-precision flex sensor interfaced with an Arduino microcontroller, simple threshold logic, driving LED indicators, and pre-recorded audio playback. To further enhance recognition accuracy and adaptability, a second module integrates a lightweight neural network trained on a diverse gesture dataset, enabling the system to detect and differentiate subtle variations in finger positions across users. Both solutions leverage flex sensors, a voltage sensor, an integrated LED indicator, and a Bluetooth module for wireless output. Prototype evaluations involving multiple users demonstrated an average real-time translation accuracy of 98%, robust response times, and high usability. By combining inexpensive hardware, open-source tools, and AI-driven classification, this work advances accessible assistive technology. Future enhancements will extend mobile connectivity, broaden the gesture vocabulary, and optimize model performance for more natural and expressive interaction.
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