Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, therefore, suitable for detecting body reactions to stress. The field of machine learning, or more precisely, deep learning, has been able to produce outstanding results. However, in order to produce these good results, large amounts of labeled data are needed, which, in the context of physiological data related to stress detection, are a great challenge to collect, as they usually require costly experiments or expert knowledge. This usually results in an imbalanced and small dataset, which makes it difficult to train a deep learning algorithm. In recent studies, this problem is tackled with data augmentation via a Generative Adversarial Network (GAN). Conditional GANs (cGAN) are particularly suitable for this as they provide the opportunity to feed auxiliary information such as a class label into the training process to generate labeled data. However, it has been found that during the training process of GANs, different problems usually occur, such as mode collapse or vanishing gradients. To tackle the problems mentioned above, we propose a Long Short-Term Memory (LSTM) network, combined with a Fully Convolutional Network (FCN) cGAN architecture, with an additional diversity term to generate synthetic physiological data, which are used to augment the training dataset to improve the performance of a binary classifier for stress detection. We evaluated the methodology on our collected physiological measurement dataset, and we were able to show that using the method, the performance of an LSTM and an FCN classifier could be improved. Further, we showed that the generated data could not be distinguished from the real data any longer.

中文翻译：

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用于在可穿戴生理传感器数据中生成用于压力检测的时间序列数据的条件 GAN

在过去的几年里，使用可穿戴传感器与机器学习相结合的以人为本的应用受到了极大的关注。与此同时，可穿戴传感器也得到了发展，现在能够准确地测量生理信号，因此适用于检测身体对压力的反应。机器学习领域，或者更准确地说，深度学习，已经能够产生出色的成果。然而，为了产生这些好的结果，需要大量的标记数据，在与压力检测相关的生理数据的背景下，收集这些数据是一个巨大的挑战，因为它们通常需要昂贵的实验或专业知识。这通常会导致数据集不平衡且较小，从而难以训练深度学习算法。在最近的研究中，这个问题是通过生成对抗网络（GAN）的数据增强来解决的。条件 GAN (cGAN) 特别适用于此，因为它们提供了将诸如类标签之类的辅助信息输入到训练过程中以生成标记数据的机会。然而，已经发现，在 GAN 的训练过程中，通常会出现不同的问题，例如模式崩溃或梯度消失。为了解决上述问题，我们提出了一个长短期记忆 (LSTM) 网络，结合全卷积网络 (FCN) cGAN 架构，以及一个额外的多样性项来生成合成生理数据，用于增强训练数据集，以提高用于压力检测的二元分类器的性能。我们在收集的生理测量数据集上评估了该方法，我们能够证明使用该方法可以提高 LSTM 和 FCN 分类器的性能。此外，我们表明生成的数据无法再与真实数据区分开来。