Exploring the Impact of Synthetic Data on Human Activity Recognition Tasks

Abstract

This paper investigates the potential of synthetic data in enhancing the performance of Machine Learning classifiers. Our focus lies primarily on the Human Activity Recognition task, Specifically through wearable device sensors. We analyze the performance of three Generative Adversarial Networks (GANs) and a Diffuse model, considering fidelity, diversity, and generalization metrics. In addition, we assess the relationship between the addition of synthetic samples to the training data and the impact of imbalanced classes on the generative model in the production of synthetic samples. We also introduce a novel GAN designed to generate synthetic samples of time series data from wearable devices. After conducting nearly 400 experiments, our results suggest that synthetic samples can significantly improve the performance of Machine Learning models when real data are scarce. More importantly, our findings underline that data quality precedes the quantity of synthetic data added to training samples.