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Device-Free User Authentication, Activity Classification and Tracking Using Passive Wi-Fi Sensing: A Deep Learning-Based Approach.
- Source :
- IEEE Sensors Journal; 8/15/2020, Vol. 20 Issue 16, p9329-9338, 10p
- Publication Year :
- 2020
-
Abstract
- Growing concerns over privacy invasion due to video camera based monitoring systems have made way to non-invasive Wi-Fi signal sensing based alternatives. This paper introduces a novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach. The novelty of the proposed solution is that the system is fully autonomous and requires zero user intervention unlike systems that require user originated initialization, or a user held transmitting device to facilitate the prediction. Experimental results demonstrate over 95% accuracy for user identification and activity recognition, while the user localization results exhibit a ±12cm error, which is a significant improvement over the existing user tracking methods that utilize passive Wi-Fi signals. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1530437X
- Volume :
- 20
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- IEEE Sensors Journal
- Publication Type :
- Academic Journal
- Accession number :
- 144714669
- Full Text :
- https://doi.org/10.1109/JSEN.2020.2987386