Human Identification Using WIFI Signal

Prior research has shown that it is possible to identify human by examining the changes on the WiFi spectrum using WiFi signals. Wireless devices fill the air with a spectrum of invisible RF (Radio Frequency) Signals. When human start walking through this spectrum the signal propagates differently for each person as everyone’s gait, body-shape, and walking style is unique and does not matches with another person. In this paper, we propose a system that uses Channel State Information (CSI) System to extract unique features of an individual’s unique walking pattern. While other identification systems (e.g. fingerprint, face recognition) has certain shortcomings (e.g. intrusive, expensive, and inconvenient), the proposed system overcomes these problems. This system can uniquely identify human with an average accuracy of 78% to 97.5% by using Random Forest (RF) and 84% to 95% average accuracy by using Boosted Decision Tree (BDT) algorithm. The authors believe this system can be used in small office or smart home settings.

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