GAN Based Noise Generation to Aid Activity Recognition when Augmenting Measured WiFi Radar Data with Simulations

This work considers the use of a passive WiFi radar (PWR) to monitor human activities. Real-time uncooperative monitoring of people has numerous applications ranging from smart cities and transport to IoT and security. In e-healthcare, PWR technology could be used for ambient assisted living and early detection of chronic health conditions. Large training datasets could drive forward machine-learning-focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Therefore, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data at multiple IEEE WiFi standards(IEEE 802.11g, n, and ad). We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured signatures. To create a more realistic training dataset, we artificially add noise to our clean simulated spectrograms. A noise distribution is directly learned from real radar measurements using a Generative Adversarial Network (GAN). We observe improvements in the classification performances between 3 to 8%. Our results suggest that simulation data can be used to make adequate training data when the available measurement training support is low.