Extracting Visual Micro-Doppler Signatures from Human Lips Motion Using UoG Radar Sensing Data for Hearing Aid Applications

Umer Saeed, Syed Aziz Shah, Yazeed Yasin Ghadi, Muhammad Zakir Khan, Ahmad Jawad, Syed Ikram Shah, Hira Hameed, Qammer Hussain Abbasi

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    This study proposes a secure and effective lips-reading system that can accurately detect lips movements, even when face masks are worn. The system utilizes radio frequency (RF) sensing and ultra-wideband (UWB) radar technology, which overcomes the challenges posed by traditional vision-based systems. By leveraging deep learning models, the system interprets lips and mouth movements and achieves an overall accuracy of 90% for both mask-on and mask-off scenarios. The study utilized a trusted dataset from the University of Glasgow (UoG), consisting of spectrograms of lips motions stating five vowels and a voiceless class from distinct participants. The cutting-edge deep learning algorithm, Residual Neural Network (ResNet50), was used for the evaluation of the dataset and achieved an 87% accurate detection rate with a mask-on scenario, which is a 14% improvement compared to prior published work. The findings of this study contribute to the development of a robust lips-reading framework that can enhance communication accessibility in applications such as hearing aids, voice-controlled systems, biometrics, and more.
    Original languageEnglish
    Pages (from-to)22111-22118
    Number of pages8
    JournalIEEE Sensors Journal
    Issue number19
    Early online date31 Aug 2023
    Publication statusPublished - 1 Oct 2023

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    This work is supported in parts by EPSRC grant (EP/W037076/1).


    • ResNet50
    • InceptionV3
    • VGG16
    • RF sensing
    • UWB radar
    • lips-reading
    • speech recognition

    ASJC Scopus subject areas

    • Instrumentation
    • Electrical and Electronic Engineering


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