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NotiFi: a ubiquitous WiFi-based abnormal activity detection system

conference contribution
posted on 2017-06-30, 00:00 authored by D Zhu, N Pang, Gang LiGang Li, S Liu
We build an ubiquitous abnormal activity detection system, namely NotiFi, for accurately detecting the abnormal activities on commercial off-the-shelf (COTS) IEEE 802.11 devices. In contrast to the traditional wearable sensor based and computer vision based systems which require additional sensors or enough lighting in line-of-sight (LoS) scenario, we proceed directly with abnormal activity characterization and activity modeling at the WiFi signal level based on Channel State Information (CSI). The intuition of NotiFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the phase and the amplitude information of Channel State Information (CSI) will change sensitively. By creating a multiple hierarchical Dirichlet processes, NotiFi automatically learns the number of human body activity categories for abnormal detection. Experimental results in three typical indoor environments indicate that NotiFi can achieve satisfactory performance in accuracy, robustness and stability.

History

Pagination

1766-1773

Location

Anchorage, Alaska

Start date

2017-05-14

End date

2017-05-19

ISBN-13

9781509061815

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

IJCNN 2017 : Proceedings of the International Joint Conference on Neural Networks 2017

Event

International Joint Conference on Neural Networks (2017 : Anchorage, Alaska)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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