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Social Touch Gesture Recognition Using Convolutional Neural Network.

Saad Albawi1,2, Oguz Bayat1, Saad Al-Azawi2

  • 1Altinbas University, Graduate School of Science and Engineering, Istanbul, Turkey.

Computational Intelligence and Neuroscience
|November 8, 2018
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Summary
This summary is machine-generated.

This study introduces a deep convolutional neural network for social touch gesture recognition using only sensor data. The system achieves competitive accuracy and faster recognition times, enhancing human-robot interaction.

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Social touch recognition is crucial for realistic human-robot interaction.
  • Existing methods often require extensive data preprocessing.

Purpose of the Study:

  • To develop a social touch gesture recognition system using deep learning.
  • To evaluate the system's performance on raw sensor data without preprocessing.

Main Methods:

  • A deep convolutional neural network (CNN) was employed for gesture recognition.
  • A dataset of social touch gestures performed on a mannequin arm was utilized.
  • Leave-one-subject-out cross-validation was used for performance evaluation.

Main Results:

  • The system achieved 63.7% classification accuracy with minimal frame data (0.2%-4.19%).
  • Recognition occurred in near real-time.
  • The proposed CNN system outperformed other algorithms in classification ratio and recognition time.

Conclusions:

  • Deep learning effectively recognizes social touch gestures from raw sensor data.
  • The system offers a competitive and efficient solution for human-robot interaction.
  • The approach minimizes data preprocessing needs, improving practical application.