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Updated: Jun 21, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Research on FBG Tactile Sensing Shape Recognition Based on Convolutional Neural Network.

Guan Lu1, Zhihui Shen1, Ting Cai1

  • 1School of Mechanical Engineering, Nantong University, Nantong 226000, China.

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|July 13, 2024
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Summary

A new fiber Bragg grating (FBG) tactile sensing array combined with convolutional neural networks (CNNs) significantly improves robot shape recognition. This method achieves high accuracy for flexible skin applications.

Keywords:
convolutional neural networkfiber Bragg gratingflexible skinshape recognitiontactile sensing array

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Area of Science:

  • Robotics
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Robot perception relies heavily on shape recognition.
  • Existing fiber tactile sensors for flexible skin have limitations in efficiency and recognition types.

Purpose of the Study:

  • To propose and evaluate a convolutional neural network (CNN)-based shape recognition method using a fiber Bragg grating (FBG) tactile sensing array.
  • To enhance the shape recognition capabilities for flexible tactile sensing in robotic applications.

Main Methods:

  • Fabrication of a tactile sensing array using flexible resin and 3D printing.
  • Construction of a shape recognition system to collect tactile data.
  • Shape classification using CNN, Random Forest, Support Vector Machine, and K-Nearest Neighbor algorithms.

Main Results:

  • The FBG tactile sensing array demonstrated good sensitivity and perception.
  • CNN achieved a shape recognition accuracy of 96.58%, outperforming other methods.
  • CNN's F1 score was 96.95%, also superior to the compared algorithms.

Conclusions:

  • The proposed CNN-based FBG tactile sensing array offers a robust solution for shape recognition in flexible robotic skin.
  • This research provides an experimental foundation for advanced shape perception in tactile sensing systems.