NDLI: Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi

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Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi

Content Provider	Semantic Scholar
Author	Arshad, Sheheryar
Copyright Year	2019
Abstract	FEATURE EXTRACTION IN NOISE-DIVERSE ENVIRONMENTS FOR HUMAN ACTIVITIES RECOGNITION USING WI-FI SHEHERYAR ALI ARSHAD, Ph.D. The University of Texas at Arlington, 2019 Supervising Professor: Yonghe Liu With the rapid development of 802.11 standard and Internet of Things (IoT) applications, Wi-Fi (IEEE 802.11) has emerged as the most widely used wireless communication technology. Wi-Fi based sensing has found widespread use cases involving activity recognition, indoor localization, design of smart spaces and in healthcare applications. This dissertation presents the study of human activities’ sensing and recognition using channel state information (CSI) of Wi-Fi. We highlight the limitations of existing methods and consequently design the frameworks for collecting stable CSI and monitoring different indoor and outdoor environments for human activities. Specifically, this dissertation provide means to define and extract quality features in different noisy environments for achieving accurate human activity recognition (HAR). In first part of the dissertation, we present WiChase framework which extracts anomalous CSI data and processes it to sense and classify surrounding human activities. Contrary to some other works that use fixed CSI window, we automate the data extraction and activity labeling based on the variance of multipleinput and multiple-output (MIMO) subcarriers. Moreover, we recognize human activities using CSI amplitude together with calibrated phase and propose the integration of these
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://rc.library.uta.edu/uta-ir/bitstream/handle/10106/28818/ARSHAD-DISSERTATION-2019.pdf?isAllowed=y&sequence=1
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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