Enhanced Human Activity Recognition Using Controllable GANs for Synthetic Data Generation

Human Activity Recognition (HAR) is essential for applications like health monitoring and fall detection using mobile sensors. However, obtaining large datasets necessary for training HAR systems is prohibitively expensive. Generative Adversarial Networks (GANs) have been proposed to generate synthetic HAR data, simplifying and enhancing the development of HAR systems. While these methods have improved accuracy, existing GAN-based approaches struggle with generating clear abnormal patterns, weakening anomaly detection capabilities. Available HAR data predominantly focuses on normal activities and does not target anomalies, making it challenging to detect anomalous situations. To address this, we propose a novel controllable GAN that generates realistic HAR data as well as distinct abnormal classes. This advancement enhances anomaly detection accuracy through more standardized activity recognition and quantification by HAR systems. We evaluate our approach on the WISDM dataset, demonstrating significant improvements in the balance and quality of synthetic data, leading to better performance in anomaly detection.