AI-Driven Human Wi-Fi Sensing with Hierarchical Architecture and Breathing Detection

This paper presents an Artificial Intelligence (AI)-powered Wi-Fi channel state information human sensing system via breathing detection as a wellness feature through a threelayer hierarchical architecture. The system combines a twostage pipeline (activity detection pre-filtering followed by breathing detection) with temporal user state tracking and semantic classification. Based on three features (Breathing-to-Noise Ratio, Subcarrier Agreement, and Nonlinearity), our activity detection model achieves 88.5% F1-score on test data, and the breathing detection model 96.47% F1-score. Operating at 20 Hz sampling rate with 8 sec processing windows, the system enables intelligent wellness monitoring while maintaining cross-dataset robustness. Extensive validation across independent datasets demonstrates consistent high accuracy, making this approach suitable for realistic deployment in presence-aware computing, wellness monitoring, and human-computer interaction applications.