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Non-invasive assistive framework for sign language recognition using software-defined radio sensing and deep

Saman Nosheen1, Ali Mustafa1, Ihtesham Jadoon1,2

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan.

Disability and Rehabilitation. Assistive Technology
|May 31, 2026
PubMed
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This study introduces a non-invasive framework for American Sign Language recognition using radio-frequency sensing and deep learning. The system achieves 98% accuracy, offering a privacy-preserving communication aid for individuals with hearing impairments.

Area of Science:

  • Assistive Technology
  • Machine Learning
  • Wireless Communication

Background:

  • Traditional American Sign Language recognition (ASLR) methods using cameras or gloves have limitations in privacy and environmental adaptability.
  • There is a need for non-invasive, privacy-preserving communication support for individuals with hearing impairments.
  • Radio-frequency (RF) sensing offers a potential alternative for capturing human motion without direct visual or physical contact.

Purpose of the Study:

  • To propose and evaluate a proof-of-concept framework for early-stage American Sign Language recognition (ASLR).
  • To explore the technical feasibility of using RF sensing and time-series deep learning (DL) for ASLR.
  • To develop a communication support mechanism that respects privacy and functions in diverse environmental conditions.
Keywords:
American Sign LanguageOFDMchannel state informationdeep learninghearing impairedsoftware-defined radio

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Main Methods:

  • Utilized software-defined radio (SDR) with Universal Software Radio Peripheral (USRP) kits to transmit RF signals.
  • Captured variations in wireless channel state information (WCSI) induced by human sign imprints.
  • Processed and classified RF data using a DL architecture optimized for time-series analysis, trained on 20 American signs.

Main Results:

  • Achieved 98% recognition accuracy for American signs using comparative analysis of time-series DL algorithms.
  • Demonstrated the framework's robustness in nonintrusive operation and low-light conditions.
  • Highlighted the interpretability of complex full-body movements captured through RF sensing.

Conclusions:

  • The proposed framework shows potential for future communication-support technologies for individuals with hearing impairments.
  • Integrating SDR frequency sensing with DL techniques can advance ASLR research.
  • This approach lays the groundwork for scalable, privacy-preserving assistive communication systems.