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Multi-Feature Transformer-Based Learning for Continuous Human Motion Recognition with High Similarity Using mmWave

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  • 1Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan.

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Summary
This summary is machine-generated.

This study introduces a new algorithm for continuous human motion recognition (CHMR) using Doppler radar, specifically addressing high similarity challenges. The novel CHMR-HS method achieves high accuracy, outperforming existing approaches for enhanced privacy and security.

Keywords:
FMCWTransformercontinuous human motion recognitionhigh similaritymulti-feature

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Area of Science:

  • * Utilizes Doppler-radar-based continuous human motion recognition (CHMR) for enhanced privacy and personal security applications.

Background:

  • * Existing CHMR methods using mmWave FMCW Radar struggle with recognizing continuous human motions that exhibit high similarity.
  • * This limitation necessitates advancements in radar-based human motion analysis.

Purpose of the Study:

  • * To propose a novel CHMR algorithm, termed CHMR-HS, designed to overcome the high similarity problem in motion recognition.
  • * To investigate the effectiveness of spectral-temporal multi-features (2D and 3D) within a modified Transformer learning model.

Main Methods:

  • * Employs background denoising and target extraction to clarify FMCW radar images.
  • * Extracts 2D features (Doppler, range, angle signatures) and 3D features (range-Doppler-time, range-angle-time signatures).
  • * Utilizes a modified Transformer-encoder and a re-designed Transformer-sequential-decoder model for distinguishing high-similarity motions.

Main Results:

  • * The CHMR-HS scheme achieved accuracies of 95.2% with 3D features and 94.5% with 2D features.
  • * Simulation results demonstrate the proposed CHMR-HS scheme's superiority over existing CHMR methods.

Conclusions:

  • * The developed CHMR-HS algorithm effectively addresses the challenge of high similarity in continuous human motion recognition.
  • * The modified Transformer-based approach shows significant advantages for mmWave FMCW Radar-based human motion analysis.