OpenFi: Open-Set WiFi Human Sensing via Virtual Embedding Confidence-Aware

WiFi sensing technology utilizes Channel State Information (CSI) to analyze human behavior and plays a crucial role in mobile computing. WiFi sensing systems are typically deployed by collecting data in specific known environments, known as closed-set settings. However, in practical deployment, WiFi sensing systems may encounter unknown environments, generating unknown CSI patterns due to signal reflection, multipath effects, and interference. In such open-set conditions, the WiFi sensing system should possess the capability to recognize unknown CSI patterns, enhancing its security and reliability. In response, this work proposes an open-set WiFi human sensing method based on virtual embedding confidence-aware (OpenFi). The core of OpenFi is virtual embedding generation to simulate a realistic open-set feature space. This strategy minimizes both empirical and open-set risks, enabling OpenFi to recognize unknown CSI patterns effectively. We conducted extensive experiments on diverse datasets, covering various WiFi sensing tasks, including human identification, human activity recognition, and sign language recognition. Experimental results demonstrate that OpenFi accurately identifies previously unseen CSI patterns in open-set conditions, achieving significant improvements of up to 27% and 10.26% in the FPR95 and OSCR metrics, respectively.