Device-Free User Authentication, Activity Classification and Tracking using Passive Wi-Fi Sensing: A Deep Learning Based Approach

Abstract--Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity cla ssification and tracking in a noninvasive manner . Existing infrastruct ure makes Wi-Fi a possible candidate, yet, utilizing tradition al signal processing methods to extract information necessary to ful ly characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel en d-to-end deep learning framework that simultaneously predic ts the identity, activity and the location of a user to create user p rofiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervent ion unlike systems that require user-initiated initializatio n, or a user held transmitting device to facilitate the prediction. The system can also predict the trajectory of the user by predicting the location of a user over consecutive time steps. The performa nce of the system is evaluated through experiments. P ARTfrom the applications related to surveillance and defense, user identification, behaviour analysis, localization and user activity recognition have become increasingly crucial tasks due to the popularity of facilities such as cashierless stores and senior citizen residences. Current state-of-the-art techniques for passive user authentication [1], re-identification [2], activity classification [3] and trackin g [4], [5] are primarily based on video feed analysis. However, due to concerns on privacy invasion, camera videos are not deeme d to be the best choice in many practical applications. Hence, there is a growing need for noninvasive alternatives. A possible alternative being considered is ambient Wi-Fi signals, which are widely available and easily accessible.