Explainable evaluation of generative adversarial networks for wearables data augmentation

Data augmentation represents an opportunity for Artificial Intelligence (AI) applications, as it aims at creating new synthetic data based on an existing baseline. In this paper, we present a new evaluation framework for Generative Adversarial Networks (GANs), a data augmentation technique, in multivariate data classification contexts. The goal is not limited to assessing the performance variations obtained through GANs, but also to inspect results with explainable AI (XAI) tools, understanding how GANs work and, finally, exploiting them to discover new knowledge. To this aim, we adopt the Logic Learning Machine (LLM) for performance assessment and rule extraction, and introduce a new measure of rule similarity to compare different artificial datasets. We apply the methodology on two case studies, activity recognition and physical fatigue detection, confirming that GANs can help in overcoming limitations of original datasets and lead to new discoveries. • Innovative use of eXplainable Artificial Intelligence (XAI) to evaluate synthetic data. • Rule similarity as measure of knowledge discovery from synthetic data. • A flexible solution with respect to the data generation method and XAI model adopted. • Applications to wearable devices data.