Exploring the Benefits of Time Series Data Augmentation for Wearable Human Activity Recognition

Wearable Human Activity Recognition (HAR) is an important field of research in smart assistive technologies. Collecting the data needed to train reliable HAR classifiers is complex and expensive. As a way to mitigate data scarcity, Time Series Data Augmentation (TSDA) techniques have emerged as a promising approach for generating synthetic HAR data. TSDA is not as trivial as image augmentation and has been relatively less investigated. In this paper, a comparative study of various state-of-the-art TSDA techniques is applied in the context of wearable HAR. More specifically, we investigate the classification of human activities on the OPPORTUNITY dataset [26] using a deep CNN-LSTM architecture trained on raw and synthetic data. Our study highlights the importance of TSDA on performance enhancement for multivariate multi-class datasets. Interestingly very simple time domain-based TSDA techniques notably outperform complex ones based on Generative Adversarial Networks. We provide practical advice on how to apply TSDA for imbalanced datasets in practice for generating the ideal amount of synthetic data to achieve optimal classification accuracy. Our TSDA-based approach outperforms the previous state-of-the-art [24] on the OPPORTUNITY dataset by and in average and weighted F1-scores, respectively.