Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information

Shi, Z; Zhang, JA; Xu, R; Fang, G

Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information

Shi, Z Zhang, JA

Xu, R Fang, G

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Publication Type:: Conference Proceeding
Citation:: 2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings, 2019
Issue Date:: 2019-02-19

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Field	Value	Language
dc.contributor.author	Shi, Z	en_US
dc.contributor.author	Zhang, JA https://orcid.org/0000-0002-6102-3762	en_US
dc.contributor.author	Xu, R	en_US
dc.contributor.author	Fang, G	en_US
dc.date.available	2020-12-14T18:04:52Z
dc.date.issued	2019-02-19	en_US
dc.identifier.citation	2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings, 2019	en_US
dc.identifier.isbn	9781538649206	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132419
dc.description.abstract	© 2018 IEEE. Channel State Information (CSI) is widely used for device free human activity recognition. Feature extraction remains as one of the most challenging tasks in a dynamic and complex environment. In this paper, we propose a human activity recognition scheme using Deep Learning Networks with enhanced Channel State information (DLN-eCSI). We develop a CSI feature enhancement scheme (CFES), including two modules of background reduction and correlation feature enhancement, for preprocessing the data input to the DLN. After cleaning and compressing the signals using CFES, we apply the recurrent neural networking (RNN) to automatically extract deeper features and then the softmax regression algorithm for activity classification. Extensive experiments are conducted to validate the effectiveness of the proposed scheme.	en_US
dc.relation.ispartof	2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings	en_US
dc.relation.isbasedon	10.1109/GLOCOMW.2018.8644435	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. Channel State Information (CSI) is widely used for device free human activity recognition. Feature extraction remains as one of the most challenging tasks in a dynamic and complex environment. In this paper, we propose a human activity recognition scheme using Deep Learning Networks with enhanced Channel State information (DLN-eCSI). We develop a CSI feature enhancement scheme (CFES), including two modules of background reduction and correlation feature enhancement, for preprocessing the data input to the DLN. After cleaning and compressing the signals using CFES, we apply the recurrent neural networking (RNN) to automatically extract deeper features and then the softmax regression algorithm for activity classification. Extensive experiments are conducted to validate the effectiveness of the proposed scheme.

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

http://hdl.handle.net/10453/130743