Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction

Shi, Z; Zhang, JA; Xu, R; Cheng, Q

Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction

Shi, Z Zhang, JA Xu, R Cheng, Q

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Communications, 2019, 2019-May
Issue Date:: 2019-05-01

Closed Access

	Filename	Description	Size
	08761445 (1).pdf	Published version	1.2 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shi, Z
dc.contributor.author	Zhang, JA
dc.contributor.author	Xu, R
dc.contributor.author	Cheng, Q
dc.date	2019-05-20
dc.date.accessioned	2020-06-10T04:54:48Z
dc.date.available	2020-06-10T04:54:48Z
dc.date.issued	2019-05-01
dc.identifier.citation	IEEE International Conference on Communications, 2019, 2019-May
dc.identifier.isbn	9781538680889
dc.identifier.issn	1550-3607
dc.identifier.uri	http://hdl.handle.net/10453/141230
dc.description.abstract	© 2019 IEEE. Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in a dynamic and complex environment. In this paper, we propose a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method. The CCFE method pre-processes the signals input to the DLN in two steps. Firstly, it uses a recursive algorithm to reduce non-activity-related information from the signal and hence enhance the activity-dependent signals. Secondly, it computes the correlation over both the time and frequency domain to disclose better signal structure and compress the signal. From such enhanced and compressed signals, we utilize the recurrent neural networking (RNN) to automatically extract deeper features, and then apply the softmax regression algorithm for classifying activities. Through extensive experimental results, our proposed scheme is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.
dc.language	en
dc.publisher	IEEE
dc.relation	iOmniscient Pty Ltd
dc.relation.ispartof	IEEE International Conference on Communications
dc.relation.ispartof	IEEE International Conference on Communications
dc.relation.isbasedon	10.1109/ICC.2019.8761445
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction
dc.type	Conference Proceeding
utslib.citation.volume	2019-May
utslib.location.activity	Shanghai, China
utslib.for	0805 Distributed Computing
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-10T04:54:41Z
pubs.finish-date	2019-05-24
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-05-20
pubs.volume	2019-May
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in a dynamic and complex environment. In this paper, we propose a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method. The CCFE method pre-processes the signals input to the DLN in two steps. Firstly, it uses a recursive algorithm to reduce non-activity-related information from the signal and hence enhance the activity-dependent signals. Secondly, it computes the correlation over both the time and frequency domain to disclose better signal structure and compress the signal. From such enhanced and compressed signals, we utilize the recurrent neural networking (RNN) to automatically extract deeper features, and then apply the softmax regression algorithm for classifying activities. Through extensive experimental results, our proposed scheme is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141230