Transformer Networks for Data Augmentation of Human Physical Activity Recognition
This work addresses the need for efficient data augmentation in sensor time-series classification, which is incremental as it builds on existing GAN methods.
The paper tackled the problem of data augmentation for human physical activity recognition by comparing transformer-based generative adversarial networks with recurrent GANs, resulting in improvements in time and computational resource savings.
Data augmentation is a widely used technique in classification to increase data used in training. It improves generalization and reduces amount of annotated human activity data needed for training which reduces labour and time needed with the dataset. Sensor time-series data, unlike images, cannot be augmented by computationally simple transformation algorithms. State of the art models like Recurrent Generative Adversarial Networks (RGAN) are used to generate realistic synthetic data. In this paper, transformer based generative adversarial networks which have global attention on data, are compared on PAMAP2 and Real World Human Activity Recognition data sets with RGAN. The newer approach provides improvements in time and savings in computational resources needed for data augmentation than previous approach.