Healthc Inform Res.  2019 Oct;25(4):344-349. 10.4258/hir.2019.25.4.344.

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

• Alnujaim I
• Kim Y

• KMID: 2462234
• DOI: http://doi.org/10.4258/hir.2019.25.4.344

Abstract

OBJECTIVES
Human motion analysis can be applied to the diagnosis of musculoskeletal diseases, rehabilitation therapies, fall detection, and estimation of energy expenditure. To analyze human motion with micro-Doppler signatures measured by radar, a deep learning algorithm is one of the most effective approaches. Because deep learning requires a large data set, the high cost involved in measuring large amounts of human data is an intrinsic problem. The objective of this study is to augment human motion micro-Doppler data employing generative adversarial networks (GANs) to improve the accuracy of human motion classification.
METHODS
To test data augmentation provided by GANs, authentic data for 7 human activities were collected using micro-Doppler radar. Each motion yielded 144 data samples. Software including GPU driver, CUDA library, cuDNN library, and Anaconda were installed to train the GANs. Keras-GPU, SciPy, Pillow, OpenCV, Matplotlib, and Git were used to create an Anaconda environment. The data produced by GANs were saved every 300 epochs, and the training was stopped at 3,000 epochs. The images generated from each epoch were evaluated, and the best images were selected.
RESULTS
Each data set of the micro-Doppler signatures, consisting of 144 data samples, was augmented to produce 1,472 synthesized spectrograms of 64 × 64. Using the augmented spectrograms, the deep neural network was trained, increasing the accuracy of human motion classification.
CONCLUSIONS
Data augmentation to increase the amount of training data was successfully conducted through the use of GANs. Thus, augmented micro-Doppler data can contribute to improving the accuracy of human motion recognition.

Keyword

Motion Perception; Data Visualization; Deep Learning; Big Data; Supervised Machine Learning

MeSH Terms

Boidae
Classification
Dataset
Diagnosis
Energy Metabolism
Human Activities
Humans*
Learning
Motion Perception
Musculoskeletal Diseases
Rehabilitation
Supervised Machine Learning

Figure

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  Figure 1 Setup for the seven human motion measurement using Doppler radar.

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  Figure 2 Procedure for setting up a path: (A) open Windows Explorer then choose Properties, (B) open advanced system setting, (C) setting up environment variables, and (D) select path then click edit.

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  Figure 3 Anaconda environment and training process: (A) creating and activating an Anaconda environment, (B) installing a package in Anaconda, and (C) running a training session.

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  Figure 4 Original micro-Doppler image of the following seven activities: (A) boxing while moving forward, (B) boxing while standing in place, (C) crawling, (D) running, (E) sitting still, (F) walking, and (G) walking hunched over while holding a stick.

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  Figure 5 Augmented micro-Doppler image using generative adversarial networks: (A) boxing while moving forward, (B) boxing while standing in place, (C) crawling, (D) running, (E) sitting still, (F) walking, and (G) walking hunched over while holding a stick.

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