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Augmentation of Doppler Radar Data Using Generative Adversarial Network for Human Motion Analysis.

Ibrahim Alnujaim1, Youngwook Kim1

  • 1Department of Electrical and Computer Engineering, California State University, Fresno, CA, USA.

Healthcare Informatics Research
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Generative adversarial networks (GANs) successfully augmented micro-Doppler radar data for human motion analysis. This data augmentation improved the accuracy of human motion classification using deep learning models.

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Area of Science:

  • Radar Signal Processing
  • Machine Learning
  • Human Motion Analysis

Background:

  • Human motion analysis using radar micro-Doppler signatures is crucial for applications like disease diagnosis, rehabilitation, and fall detection.
  • Deep learning algorithms are effective for analyzing this data but require extensive datasets, posing a significant cost barrier.
  • Generative adversarial networks (GANs) offer a potential solution for data augmentation to overcome data scarcity.

Purpose of the Study:

  • To augment human motion micro-Doppler data using generative adversarial networks (GANs).
  • To enhance the accuracy of human motion classification by utilizing GAN-generated data.
  • To address the challenge of high costs associated with collecting large datasets for deep learning in human motion analysis.

Main Methods:

  • Collected authentic micro-Doppler radar data for 7 distinct human activities, with 144 samples per activity.
  • Utilized a software environment including GPU drivers, CUDA, cuDNN, Anaconda, Keras-GPU, SciPy, Pillow, OpenCV, and Matplotlib for GAN training.
  • Trained GANs for 3,000 epochs, selecting the best generated spectrograms (64x64) from each 300-epoch interval.

Main Results:

  • Augmented each 144-sample dataset to generate 1,472 synthesized spectrograms.
  • Trained a deep neural network using the augmented spectrograms.
  • Observed an increase in the accuracy of human motion classification after training with the augmented data.

Conclusions:

  • Successfully demonstrated data augmentation for micro-Doppler human motion data using GANs.
  • Augmented data significantly contributes to improving the accuracy of human motion recognition.
  • GAN-based data augmentation presents a viable method to overcome data limitations in radar-based human motion analysis.