Alnujaim Ibrahim, Kim Youngwook
Department of Electrical and Computer Engineering, California State University, Fresno, CA, USA.
Healthc Inform Res. 2019 Oct;25(4):344-349. doi: 10.4258/hir.2019.25.4.344. Epub 2019 Oct 31.
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.
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.
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.
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.
人体运动分析可应用于肌肉骨骼疾病的诊断、康复治疗、跌倒检测以及能量消耗估计。为了利用雷达测量的微多普勒特征分析人体运动，深度学习算法是最有效的方法之一。由于深度学习需要大量数据集，测量大量人体数据所涉及的高成本是一个固有问题。本研究的目的是采用生成对抗网络（GAN）来扩充人体运动微多普勒数据，以提高人体运动分类的准确性。
为了测试GAN提供的数据增强效果，使用微多普勒雷达收集了7种人类活动的真实数据。每种运动产生144个数据样本。安装了包括GPU驱动程序、CUDA库、cuDNN库和Anaconda在内的软件来训练GAN。使用Keras-GPU、SciPy、Pillow、OpenCV、Matplotlib和Git创建了一个Anaconda环境。GAN生成的数据每300个epoch保存一次，训练在3000个epoch时停止。对每个epoch生成的图像进行评估，并选择最佳图像。
由144个数据样本组成的每个微多普勒特征数据集被扩充，以生成1472个64×64的合成频谱图。使用扩充后的频谱图训练深度神经网络，提高了人体运动分类的准确性。
通过使用GAN成功地进行了数据增强以增加训练数据量。因此，扩充后的微多普勒数据有助于提高人体运动识别的准确性。
