  Human activity recognition (HAR) holds significant importance in smart homes,
security, and healthcare. Existing systems face limitations because of the
insufficient spatial diversity provided by a limited number of antennas.
Furthermore, inefficiencies in noise reduction and feature extraction from
sensing data pose challenges to recognition performance. This study presents a
reconfigurable intelligent surface (RIS)-assisted passive human activity
recognition (RISAR) method, compatible with commercial Wi-Fi devices. RISAR
leverages a RIS to enhance the spatial diversity of Wi-Fi signals, effectively
capturing a wider range of information distributed across the spatial domain. A
novel high-dimensional factor model based on random matrix theory is proposed
to address noise reduction and feature extraction in the temporal domain. A
dual-stream spatial-temporal attention network model is developed to assign
variable weights to different characteristics and sequences, mimicking human
cognitive processes in prioritizing essential information. Experimental
analysis shows that RISAR significantly outperforms existing HAR methods in
accuracy and efficiency, achieving an average accuracy of 97.26%. These
findings underscore RISAR's adaptability and potential as a robust activity
recognition solution in real environments.
