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Data augmentation is a common practice to help generalization in theprocedure of deep model training. In the context of physiological time seriesclassification, previous research has primarily focused on label-invariant dataaugmentation methods. However, another class of augmentation techniques(\textit{i.e., Mixup}) that emerged in the computer vision field has yet to befully explored in the time series domain. In this study, we systematicallyreview the mix-based augmentations, including mixup, cutmix, and manifoldmixup, on six physiological datasets, evaluating their performance acrossdifferent sensory data and classification tasks. Our results demonstrate thatthe three mix-based augmentations can consistently improve the performance onthe six datasets. More importantly, the improvement does not rely on expertknowledge or extensive parameter tuning. Lastly, we provide an overview of theunique properties of the mix-based augmentation methods and highlight thepotential benefits of using the mix-based augmentation in physiological timeseries data.

Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data