A GAN-based data augmentation method for human activity recognition via the caching ability

Sensor-based human activity recognition (HAR) provides vital information for an ocean of health care and entertainment applications. However, data acquisition of such studies usually requires huge time and manpower, which delays the progress of the whole project. Data augmentation is a promising way to solve this issue. In this paper, we expand the origin dataset via a GAN-based augmentation neural network. The experimental results suggest that the generated data has a certain substitution and complementary effect in terms of the real data based on the caching deployment ability. In addition, the quasi-real data also gains good performance on identifying certain activities.