Wi-Fi Sensing for Human Identification through ESP32 Devices: an Experimental Study

Recent studies explore the possibility of detecting events in a room via Wi-Fi Sensing. This practice exploits the interaction between waves carrying Wi-Fi signals and the elements present in an environment. These interactions are called Channel State Information (CSI) and can be analyzed and exploited to infer information about the environment, such as "device-free" Human Activity Recognition, Human Identification, and more. Considering identification, we recently saw an increasing trend in the usage of low-end devices such as ESP32. Being small and low-power, they are cheap and versatile, however, the quality of the collected data is inferior. In this work, we use state-of-the-art tools to perform Human Identification using the ESP32. Software is created to act as an interface between the collected data and the algorithms suitable for Wi-Fi Sensing. To evaluate the final design, we performed a data collection in a controlled environment. The experiments show an accuracy of 95% in distinguishing two users while 74% in distinguishing three.