Development and Evaluation of Wearable Smart Gloves for Real-time Sign Language Translation
Year: 2025
Type: article
Abstract: Aims: To design and execute a two-way sign language translator aimed at enhancing communication between hearing and deaf-Dumb individuals. The device seeks to address accessibility challenges by accurately translating sign language into audio for hearing people. Study Design: A technological research and development study focused on gesture recognition and audio generation. Place and Duration of Study: Conducted at [The Higher Institute of Engineering and Technology in New Damietta.] over [January 2024 to December 2024]. Methodology: The translator uses a hand motion detection technology to interpret sign language and generates corresponding text outputs. For reverse translation, the device converts textual input into audio using a pre-designed audio database, including both hearing and deaf-Dumb individuals, to validate usability and accuracy. Performance was assessed based on parameters like recognition accuracy and ease of use. Results: User testing demonstrated high device accuracy, Participants rated the device as user-friendly and effective in facilitating communication. The findings underline the potential of the system in bridging communication gaps in everyday interactions for deaf-Dumb individuals. Challenges: One of the challenges encountered during the development of this device was effectively communicating its functionality to the target users and ensuring their adaptation to its use. Conclusions: This two-way sign language translator presents a promising solution to improve accessibility and communication for deaf-Dumb individuals. It combines innovative gesture recognition with intuitive audio generation to address a significant societal need. Looking forward, our vision is to see this innovation adopted broadly, not only by individuals with disabilities but also by the general population, fostering a more inclusive society.
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409456272