Data augmentation is a common practice to help generalization in the procedure of deep model training. In the context of physiological time series classification, previous research has primarily focused on label-invariant data augmentation methods. However, another class of augmentation techniques (\textit{i.e., Mixup}) that emerged in the computer vision field has yet to be fully explored in the time series domain. In this study, we systematically review the mix-based augmentations, including mixup, cutmix, and manifold mixup, on six physiological datasets, evaluating their performance across different sensory data and classification tasks. Our results demonstrate that the three mix-based augmentations can consistently improve the performance on the six datasets. More importantly, the improvement does not rely on expert knowledge or extensive parameter tuning. Lastly, we provide an overview of the unique properties of the mix-based augmentation methods and highlight the potential benefits of using the mix-based augmentation in physiological time series data.

翻译：数据增强是深度模型训练过程中帮助提升泛化能力的常用方法。在生理时间序列分类的背景下，以往的研究主要关注标签不变的数据增强方法。然而，另一类源于计算机视觉领域的增强技术（即 Mixup）在时间序列领域中尚未得到充分探索。本研究系统性地回顾了基于混合的增强方法（包括 mixup、cutmix 和 manifold mixup）在六个生理数据集上的应用，评估了它们在不同感官数据和分类任务中的性能。我们的结果表明，这三种基于混合的增强方法能够持续提升在六个数据集上的性能。更重要的是，这种提升不依赖于专家知识或大量参数调优。最后，我们概述了基于混合的增强方法的独特性质，并强调了在生理时间序列数据中使用基于混合的增强方法的潜在优势。