Gait and Respiration-Based User Identification Using Wi-Fi Signal

The ever-growing security issues in various scenarios create an urgent demand for a reliable and convenient identification system. Traditional identification systems request users to provide passwords, fingerprints, or other easily stolen information. Existing works show that everyone's gait and respiration have unique characteristics and are difficult to imitate. But these works only use gait or respiration information to achieve identification, which leads to low accuracy or long identification time. And they have no strong anti-interference ability, which leads to the limitation in practical application. Toward this end, we propose a new system which uses both gait and respiratory biometric characteristics to achieve user identification using Wi-Fi (GRi-Fi) in the presence of interferences. In our system, we design a segmentation algorithm to segment gait and respiration data. And we design a weighted subcarrier screening method to improve the anti-interference ability. In order to shorten the identification time, we propose a feature integration method based on the weighted average. Finally, we use a deep learning method to identify users accurately. Experimental results show that GRi-Fi can identify the users identity with an average accuracy of 98.3% in noninterference environments. Even in the presence of multiple interferences, the average identification accuracy also reaches 91.2%. In future applications, our system can be applied to many fields of Internet of Things, such as smart home systems and clocking in at companies.