| Autor: |
Jayasundara, Vinoj, Jayasekara, Hirunima, Samarasinghe, Tharaka, Hemachandra, Kasun T. |
| Zdroj: |
IEEE Sensors Journal; 8/15/2020, Vol. 20 Issue 16, p9329-9338, 10p |
| Abstrakt: |
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] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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