Wi Fi Sensing

Wi-Fi sensing repurposes existing wireless infrastructure to passively monitor human activity and environmental changes by analyzing fluctuations in channel state information (CSI) and other signal characteristics. Current research emphasizes developing robust and adaptable models, often employing deep learning architectures like Siamese networks, transformers, and generative adversarial networks (GANs), to overcome challenges such as data scarcity, domain shift, and package loss. This field is significant because it offers a cost-effective, privacy-preserving alternative to traditional sensing methods, with applications ranging from activity recognition in robotics and healthcare to gesture recognition and indoor localization.