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

TL;DR

This paper presents a deep learning framework that uses passive Wi-Fi sensing to noninvasively authenticate users, classify activities, and track locations, offering a privacy-preserving alternative to video surveillance.

Contribution

It introduces an end-to-end deep learning system capable of simultaneously predicting user identity, activity, and location without user intervention or additional devices.

Findings

High accuracy in user identification, activity classification, and tracking.

Autonomous system requiring no user-initiated setup.

Effective trajectory prediction over time.

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 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.