Smart Glove for Sign Language

Ahmad Kamel Al-Sadeq; Faiq A

Smart Glove for Sign Language

dc.contributor.author	Ahmad Kamel Al-Sadeq
dc.contributor.author	Faiq A
dc.date.accessioned	2026-10-05T10:55:54Z
dc.date.issued	2026-06-18
dc.description	--
dc.description.abstract	The communication gap between the deaf and hard-of-hearing community and the rest of society remains a significant socio-technical challenge. Sign language is a rich and structured visual language, yet it is rarely understood by the general public, leading to daily isolation and accessibility barriers for its users. This project presents the implementation of an assistive technology solution: an interactive, sensor-integrated wearable glove designed to bridge this communication divide. By capturing hand gestures and finger articulations in real-time, the system maps physical signs directly to corresponding linguistic text and audio outputs via a dedicated mobile application. The core philosophy of this project is to leverage low-cost embedded systems and physical computing to provide an intuitive, reliable, and portable translation tool that empowers sign language speakers in everyday interactions.
dc.description.sponsorship	--
dc.description.statementofresponsibility	Traditional methods of sign language translation often rely on human interpreters or computer-vision setups. While human interpretation is accurate, it is highly costly, scarce, and lacks immediate personal privacy. On the other hand, vision-based translation systems require stable lighting conditions, fixed camera configurations, and heavy processing power, which severely limits their mobility and outdoor usability. Consequently, there is a critical need for a localized, self-contained, and wearable device that operates independently of environmental lighting and complex camera tracking. The problem addressed by this research is the reliable acquisition and classification of dynamic hand postures using a hardware-constrained platform. Without a precise method to capture micro-movements of fingers and spatial orientation simultaneously, automated translation suffers from high latency and low accuracy, which frustrates the user and impedes natural conversation flow.
dc.description.tableofcontents	he primary focus of this implementation phase is to transition from conceptual architectural design to a fully functional, synchronized prototype. The specific engineering objectives are defined as follows: • Hardware Interfacing and Synchronization: Successfully integrate a network of resistive flex sensors and the MPU6050 inertial sensor with the ESP32 microcontroller, ensuring stable analog-to- digital data conversion (ADC) and noise filtering. • Algorithm Deployment: Develop and optimize a lightweight rule- based and threshold-driven classification algorithm on the embedded processor to categorize distinct gestures with minimal computational delay based on raw flex values and MPU6050 motion data.
dc.format.medium	Other
dc.identifier.citation	--
dc.identifier.issn	--
dc.identifier.other	12027739
dc.identifier.uri	https://hdl.handle.net/20.500.11888/21420
dc.language.iso	en
dc.publisher	Dr. Haneen Al-Autt
dc.relation.ispartofseries	--; --
dc.subject.classification	Health
dc.supervisor	Dr. Haneen Al-Autt
dc.title	Smart Glove for Sign Language
dc.title.alternative	--
dc.type	Graduation Project
person.telephone	+970598369723