A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment.
A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment.
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一种为中风后康复评估生成异构且真实的时间序列数据的新模型。
DOI：
10.1109/tnsre.2023.3283045
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Boukhennoufa I
中科院分区：
文献类型：
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作者：
Boukhennoufa I
基于机器学习的远程康复的应用面临着数据可用性有限的挑战。为了克服这一挑战，通常采用数据增强技术来生成反映真实的数据的配置的合成数据。其中一种有前途的数据增强技术是生成对抗网络（GAN）。然而，GAN被发现会遭受模式崩溃，这是一个常见的问题，即生成的数据无法从原始数据集中捕获所有相关信息。在本文中，我们的目标是解决基于GAN的中风后评估数据增强技术中的模式崩溃问题。我们应用GAN为两个中风后康复数据集生成合成数据，并观察到原始GAN如预期的那样遭受模式崩溃。为了解决这个问题，我们提出了一个时间序列暹罗GAN（TS-SGAN），它采用了暹罗网络和一个额外的网络。我们的分析，使用最长公共子序列（LCSS），表明TS-SGAN生成的数据均匀的两个测试数据集的所有元素，与原来的GAN。为了进一步评估TS-SGAN的有效性，我们使用Gramian Angular Field将生成的数据集编码为图像，并使用ResNet-18对其进行分类。我们的研究结果表明，TS-SGAN实现了显着的准确性提高的分类精度（35.2%-42.07%）为两个选定的数据集。这代表了对原始GAN的实质性改进。
The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techniques for post-stroke assessment. We applied the GAN to generate synthetic data for two post-stroke rehabilitation datasets and observed that the original GAN suffered from mode collapse, as expected. To address this issue, we propose a Time Series Siamese GAN (TS-SGAN) that incorporates a Siamese network and an additional discriminator. Our analysis, using the longest common sub-sequence (LCSS), demonstrates that TS-SGAN generates data uniformly for all elements of two testing datasets, in contrast to the original GAN. To further evaluate the effectiveness of TS-SGAN, we encode the generated dataset into images using Gramian Angular Field and classify them using ResNet-18. Our results show that TS-SGAN achieves a significant accuracy increase of classification accuracy (35.2%-42.07%) for both selected datasets. This represents a substantial improvement over the original GAN.