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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 LiuWe 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.
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Event
International Joint Conference on Neural Networks (2017 : Anchorage, Alaska)Pagination
1766 - 1773Publisher
IEEELocation
Anchorage, AlaskaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2017-05-14End date
2017-05-19ISBN-13
9781509061815Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2017, IEEETitle of proceedings
IJCNN 2017 : Proceedings of the International Joint Conference on Neural Networks 2017Usage metrics
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