One of the important factors of high accuracy of deep learning is the sufficient amount of training data. A robust and reliable model needs a vast amount of data to precisely capture the underlying pattern of the data. However, data collection in activity recognition is expensive and time-consuming especially from the elderly people. In this research, we propose a unified deep generative model based on the conditional generative adversarial network (CGAN). The proposed model is able to generate verisimilar sensor data of different activities using a single model. The generated data allows for data augmentation in HAR classification problem to improve its recognition accuracy. Interested readers are referred to  for more details.
 M. H. Chan, M. H. M. Noor, “A Unified Generative Model using Generative Adversarial Network for Activity Recognition”, Journal of Ambient Intelligence and Humanized Computing, 2020, doi,pdf,read