British Sign Language Detection Using Ultra-Wideband Radar Sensing and Residual Neural Network

Umer Saeed, Syed Aziz Shah, Yazeed Yasin Ghadi, Hira Hameed, Syed Ikram Shah, Jawad Ahmad, Qammer H. Abbas

Research output: Contribution to journalArticlepeer-review

Abstract

This study represents a significant advancement in Sign Language Detection (SLD), a crucial tool for enhancing communication and fostering inclusivity among the hearing-impaired community. It innovatively combines radar technology with deep learning techniques to develop a sophisticated, non-invasive SLD system. Traditional SLD methods often rely on cumbersome wearable devices or struggle with environmental inconsistencies. In contrast, this system utilizes the distinctive ability of radar to function effectively across various lighting conditions. The core of this research lies in its application to British Sign Language (BSL) detection, employing advanced neural network architectures for real-time interpretation. A key highlight is the impressive 92% accuracy rate achieved in BSL recognition, utilizing the Residual Neural Network (ResNet) model. This success is attributed to a comprehensive dataset and the strategic adaptation of ResNet for processing radar data. The fusion of radar technology with deep learning in this context not only marks a novel approach in the field but also establishes this research as a foundational contribution to the realm of SLD. Its implications extend beyond technical achievement, offering a more accessible and inclusive communication alternative for the hearing-impaired.
Original languageEnglish
Pages (from-to)(In-Press)
Number of pages8
JournalIEEE Sensors Journal
Volume(In-Press)
DOIs
Publication statusE-pub ahead of print - 15 Feb 2024

Funder

Engineering and Physical Research Council (EPSRC) (Grant Number: EP/W037076/1)

Keywords

  • British sign language
  • radar sensing
  • gesture-reading
  • contactless detection
  • ResNet

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