DSFi-Net: action recognition with double-stream feature fusion network based on WiFi CSI

In the current wireless communication system, WiFi-based wireless sensing is considered as one of the most promising wireless sensing technologies with its extensive deployment and many applications. However, the existing models and methods have some problems, such as low recognition accuracy and poor robustness. To solve the problem that the extraction of human activity information as features are affected by environmental noise and human body posture, and does not take into account the different particle size effects of different actions in different parts of the human body. This paper proposes an action component extraction method based on CSI signal decomposition to extract dynamic components independent of environment and body posture. Finally, a fusion network based on two-stream feature extraction is designed to integrate multi-scale spatial features and temporal features into multi-space temporal features. A large number of experiments are carried out to evaluate the performance of the proposed method, and the experimental results show that the proposed method has a high recognition accuracy.