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Decoding lip language using triboelectric sensors with deep learning.

Yijia Lu1, Han Tian1, Jia Cheng1

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Summary
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This study introduces a novel lip-language decoding system using flexible sensors and AI. The system achieves 94.5% accuracy, offering a promising communication tool for individuals with speech impairments.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Lip language offers a hands-free communication method for individuals with vocal impairments.
  • Collecting and interpreting lip language presents significant challenges.
  • Existing systems may lack the necessary flexibility, cost-effectiveness, or accuracy.

Purpose of the Study:

  • To develop a novel, self-powered, low-cost, flexible triboelectric sensor system for lip-language decoding.
  • To train a deep learning model for accurate interpretation of lip movements.
  • To demonstrate the feasibility of the system in practical applications.

Main Methods:

  • Fabrication and characterization of flexible triboelectric sensors for lip motion detection.
  • Collection and comparison of lip motion data for various speech types (vowels, words, phrases, silent speech).
  • Development and training of a dilated recurrent neural network model based on prototype learning.

Main Results:

  • The flexible sensors exhibit suitable structural and electrical properties for lip motion detection.
  • The prototype learning model achieved a test accuracy of 94.5% for 20 classes with 100 samples each.
  • Demonstrated successful applications in identity recognition, toy car control, and lip-to-speech conversion.

Conclusions:

  • The developed lip-language decoding system shows high accuracy and feasibility.
  • This technology offers a promising avenue for barrier-free communication and enhanced quality of life for individuals with speech disabilities.
  • The system has broad potential applications in human-computer interaction and assistive technology.