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.

本研究系统评估了混合型数据增强方法在生理时间序列分类中的性能，并发现混合型增强方法能够显著改善数据集的性能，而且不需要专业知识或大量参数调整。此外，还总结了混合型增强方法的独特属性，并强调在生理时间序列数据中使用混合型增强方法的潜在益处。