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  • 1School of Software, Xinjiang University, Urumqi 830091, China.

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This study introduces RF-alphabet, a novel RFID-based gesture recognition system for communication. It accurately recognizes 26 English letters using a unique dual-tag, dual-antenna design and efficient signal processing.

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Area of Science:

  • Human-Computer Interaction (HCI)
  • Wireless Communication Technology
  • Signal Processing

Background:

  • Gesture recognition aids communication for individuals with speech impairments.
  • Passive Radio-Frequency Identification (RFID) based gesture recognition is an emerging research area.
  • Existing methods often require large datasets and long training times for deep learning models.

Purpose of the Study:

  • To develop a low-cost, non-invasive, and scalable gesture recognition system.
  • To implement a system capable of recognizing 26 English letters with fine-grained detail.
  • To overcome limitations of traditional deep learning approaches in gesture recognition.

Main Methods:

  • Designed a dual-tag, dual-antenna layout for comprehensive gesture data capture.
  • Developed a Difference Threshold Similarity Calculation prediction model for real-time signal feature analysis.
  • Utilized phase value comparison of feature points to differentiate confusable letter signals.

Main Results:

  • Achieved an average accuracy of 90.28% for new users across different domains.
  • Demonstrated 89.7% accuracy in new environments, proving robustness.
  • Successfully implemented RF-alphabet for real-time, domain-independent recognition of 26 English letters.

Conclusions:

  • The RF-alphabet system offers a scalable and effective solution for gesture recognition.
  • The proposed methods enable efficient feature extraction and real-time analysis, reducing training demands.
  • The system proves robust and accurate for diverse users and environments, advancing HCI technology.