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Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar.

Weiqing Bai1, Siyu Chen1, Jialiang Ma1

  • 1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary

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This study introduces a ResNet Long Short-Term Memory with Attention (RLA) algorithm for improved millimeter-wave radar gesture recognition. The RLA method enhances accuracy by effectively processing complex multi-feature and single-feature data.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Millimeter-wave (mmWave) radar enables non-contact gesture recognition.
  • Existing neural network approaches face challenges with multi-feature complexity and single-feature performance limitations.

Purpose of the Study:

  • To develop an advanced gesture recognition algorithm for mmWave radar systems.
  • To overcome the limitations of current methods in handling complex and sparse gesture data.

Main Methods:

  • A novel algorithm named ResNet Long Short-Term Memory with Attention (RLA) was proposed.
  • Signal processing involved extracting range and velocity features to create range-Doppler maps.
  • Network architecture integrated residual networks with channel and spatial attention modules, alongside a Long Short-Term Memory network for temporal feature processing.
Keywords:
deep learninggesture recognitionmillimeter-wave radarsignal preprocessing

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Main Results:

  • The RLA algorithm demonstrated enhanced focus on critical gesture features by employing a residual attention mechanism.
  • High recognition accuracy was achieved even with single-feature inputs due to the Long Short-Term Memory component.
  • Experimental results confirmed superior recognition performance compared to existing methods.

Conclusions:

  • The proposed RLA algorithm offers a robust solution for mmWave radar-based gesture recognition.
  • The integration of attention mechanisms and Long Short-Term Memory networks significantly improves accuracy and feature utilization.
  • This approach effectively addresses the challenges of data complexity and performance variability in gesture recognition.