Radio-frequency (RF) sensing for deep awareness of human physical status

Abstract

This project explores the use of WiFi-based Radio-Frequency (RF) sensing for human activity recognition (HAR), enabling deep awareness of human physical status. Traditional HAR methods rely on wearable sensors or vision-based systems, which can be intrusive, impractical, or constrained to idealized environments. This study leverages Channel State Information (CSI) extracted from WiFi signals to recognize human activities involving multiple individuals using standard household WiFi routers and smartphones. A comprehensive dataset was collected using commercially available WiFi devices, capturing various activities in dynamic conditions. Several deep learning models were implemented and compared, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Attention-Based GRU (ABGRU), CNN-GRU, Siamese GRU (SGRU), and Transformer-based networks. The results demonstrate that the top-performing models achieve over 92\% accuracy in activity classification. This research underscores the feasibility of WiFi-based HAR for applications such as smart homes, elderly monitoring, and security systems. Future work will focus on expanding the dataset, improving model robustness in noisy environments, and enhancing real-world deployment feasibility.