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Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module.

Mateusz Chmurski1,2, Gianfranco Mauro1,3, Avik Santra1

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Summary

This study introduces a novel frequency-modulated continuous wave (FMCW) radar system for hand gesture recognition. The system achieves 98.13% accuracy, offering a convenient human-computer interaction (HCI) solution for resource-constrained environments.

Keywords:
Edge TPUFMCWdeep learningedge computingneural networksoptimizationquantizationradar

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Human-Computer Interaction

Background:

  • Current hand-based human-computer interaction (HCI) methods face limitations due to lighting and environmental factors.
  • Existing systems are often not suitable for resource-constrained or low-power applications.

Purpose of the Study:

  • To develop a novel, accurate, and efficient hand gesture recognition system for HCI.
  • To overcome the limitations of existing HCI technologies in diverse environments.

Main Methods:

  • A new hand gesture recognition system utilizing frequency-modulated continuous wave (FMCW) radar.
  • A simplified radar preprocessing technique preserving essential gesture information.
  • A deep neural classifier incorporating a novel Depthwise Expansion Module, optimized for the Coral Edge TPU board.

Main Results:

  • The system achieved a high classification accuracy of 98.13% for eight distinct hand gestures.
  • Demonstrated superior performance compared to state-of-the-art systems.
  • Successfully deployed on a low-power, resource-constrained Coral Edge TPU board.

Conclusions:

  • The proposed FMCW radar-based system offers a robust and accurate solution for hand gesture recognition.
  • This technology enables convenient HCI in challenging, resource-limited settings.
  • The novel deep neural classifier and preprocessing method contribute to improved recognition accuracy and efficiency.