  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.
