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  6. Bio-inspired Spiking Tactile Sensing System For Robust Texture Recognition Across Varying Scanning Speeds In Passive Touch

Bio-Inspired spiking tactile sensing system for robust texture recognition across varying scanning speeds in passive touch

Fatemeh Yavari1, Ali Motie Nasrabadi2, Fereidoun Nowshiravan Rahatabad3

  • 1Institute of Medical Science and Technologies, SR.C, Islamic Azad University, Tehran, Iran. fyavari2022a@gmail.com.

Biological Cybernetics|June 14, 2025

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View abstract on PubMed

Summary

This study introduces a novel bio-inspired tactile sensor that accurately identifies texture and speed simultaneously. The system achieves 93% accuracy, offering a robust solution for robotic and prosthetic applications.

Area of Science:

  • Robotics and Artificial Intelligence
  • Neuroscience and Bio-inspired Engineering

Background:

  • Tactile sensing is vital for texture recognition, but scanning speed variations complicate accurate discrimination.
  • Scanning speed affects texture-induced vibration frequencies, requiring effective speed encoding methods.

Purpose of the Study:

  • To develop a bio-inspired spiking tactile sensing system for joint texture and velocity encoding.
  • To enable tactile recognition in both active and passive touch without external speed sensors.

Main Methods:

  • Integrated mechanoreceptor responses with coincidence detector neurons.
  • Leveraged spike timing information from mechanoreceptors for encoding.
  • Introduced Gaussian noise to evaluate model robustness.

Main Results:

  • Achieved 93% accuracy in jointly classifying texture and speed.
  • Demonstrated stable accuracy with minimal degradation under varying noise levels.
  • The system functions effectively in both active and passive touch scenarios.

Conclusions:

  • The proposed system offers a biologically plausible solution for real-world tactile sensing challenges.
  • Provides a robust framework for texture recognition in prosthetics, robotic hands, and autonomous systems.
Keywords:
Array sensorsHaptic sensorsMachine learningSpeed detectionSpiking neural networkTactile mechanoreceptorsTexture discrimination

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