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An Embedded System for Collection and Real-time Classification of a Tactile Dataset.

Olcay Kursun1, Ahmad Patooghy1

  • 1Department of Computer Science, University of Central Arkansas, AR, USA, 72035.

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Summary

This study presents an efficient embedded system for real-time tactile perception, enabling accurate material property classification for robotics and prosthetics. The novel method reduces computational load, ensuring high accuracy with simpler classifiers.

Keywords:
Edge computingMachine learningSignal processing algorithmsTactile sensorsTexture analysis

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Embedded Systems Engineering
  • Sensory Neuroscience

Background:

  • Real-time tactile perception is crucial for dexterous manipulation in robotics, prosthetics, and augmented reality.
  • Existing embedded systems face challenges in efficiently extracting and classifying material properties from tactile sensor data.
  • The wide range of psychophysical dimensions necessitates optimized signal processing for embedded tactile perception.

Purpose of the Study:

  • To develop and validate embedded systems for real-time vibrotactile stimulation and signal recording.
  • To implement an efficient, memory-less signal feature extraction method for real-time processing.
  • To achieve high-accuracy tactile texture classification on embedded systems using an ensemble of sensors.

Main Methods:

  • Development of two embedded systems: one for vibrotactile stimulation and one for signal recording and classification.
  • Offline data quality verification using Fourier transform for feature extraction and machine learning classifiers (SVMs, neural networks).
  • Implementation of a memory-less feature extraction method for real-time data processing.

Main Results:

  • The proposed memory-less feature extraction significantly reduces computational complexity.
  • High classification accuracy was maintained even with less complex classifiers like random forests.
  • Demonstrated the feasibility of low-cost, highly accurate, real-time tactile texture classification using an ensemble of sensors.

Conclusions:

  • The developed embedded system enables efficient real-time tactile perception for material property analysis.
  • The memory-less feature extraction method is computationally efficient and effective for embedded applications.
  • This approach facilitates the creation of advanced robotic and prosthetic systems with enhanced sensory feedback.