  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.
