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Ultrasonic Touch Sensing System Based on Lamb Waves and Convolutional Neural Network.

Cheng-Shen Chang1, Yung-Chun Lee1,2

  • 1Department of Mechanical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan.

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|May 8, 2020
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Summary
This summary is machine-generated.

This study introduces a novel tactile position sensing system using acoustic waves and artificial intelligence. The system achieves over 95% accuracy in detecting touch locations on a steel plate via a convolutional neural network (CNN).

Keywords:
Lamb waveconvolutional neural networkpiezoelectric transducerssteel platetactile position sensingultrasound

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Accurate tactile sensing is crucial for human-computer interaction and robotics.
  • Existing systems often face limitations in resolution, response time, or complexity.

Purpose of the Study:

  • To develop and validate a high-accuracy tactile position sensing system.
  • To leverage acoustic wave propagation and artificial intelligence for precise touch localization.

Main Methods:

  • Utilized a steel plate with piezoelectric transducers to generate and detect Lamb waves.
  • Employed a convolutional neural network (CNN) model for analyzing waveform perturbations caused by touch.
  • Trained the CNN model using experimental data from an artificial finger and validated with human touch.

Main Results:

  • Achieved a tactile sensing accuracy exceeding 95%.
  • Demonstrated a spatial resolution of 1 × 1 cm².
  • Reported a rapid response time of 60 ms.

Conclusions:

  • The proposed acoustic wave-based tactile sensing system with AI analysis offers a promising solution for accurate and fast touch detection.
  • The system's performance indicates its potential for integration into various interactive and robotic applications.