Device-Free User Authentication, Activity Classification and Tracking using Passive Wi-Fi Sensing: A Deep Learning Based Approach
This addresses privacy concerns in surveillance by providing a camera-free alternative for user monitoring, though it appears incremental as it builds on existing Wi-Fi sensing with a new deep learning approach.
The paper tackles the problem of noninvasive user authentication, activity classification, and tracking by using passive Wi-Fi sensing, introducing a deep learning framework that simultaneously predicts identity, activity, and location with zero user intervention, achieving performance evaluated through experiments.
Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner. Existing infrastructure makes Wi-Fi a possible candidate, yet, utilizing traditional signal processing methods to extract information necessary to fully characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel end to-end deep learning framework that simultaneously predicts the identity, activity and the location of a user to create user profiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervention unlike systems that require user-initiated initialization, 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 performance of the system is evaluated through experiments.