Wifi Channel State Information Sensing based on Introspective Metric Learning

Recent advancements in WiFi Channel State Information (CSI) sensing have paved the way for robust activity recognition and human identification; however, these methods often struggle with the inherent uncertainty due to environmental noise and the variability in human activity patterns. To address these challenges, we introduce an introspective deep metric learning (IDML) framework adapted for WiFi CSI Sensing. By leveraging an uncertainty-aware embedding, our approach not only captures the semantic features of human activities but also their associated uncertainties. This dual embedding system enables our model to better distinguish between different activities and individuals, even under noisy or ambiguous conditions. We propose a novel introspective similarity metric that considers both semantic differences and ambiguities, allowing for more accurate and robust classification and identification. Our method was evaluated using several standard datasets for activity recognition and human identification, where it demonstrated superior performance over traditional methods, particularly in handling complex scenarios with high ambiguity and noise. This work not only enhances the capabilities of WiFi CSI Sensing but also sets a new benchmark for uncertainty modeling in activity and identity recognition systems.