WiFi-Based Activity Recognition using Activity Filter and Enhanced Correlation with Deep Learning

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

WiFi-Based Activity Recognition using Activity Filter and Enhanced Correlation with Deep Learning

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

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020, 00, pp. 1-6
Issue Date:: 2020-07-21

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Field	Value	Language
dc.contributor.author	Shi, Z
dc.contributor.author	Zhang, JA
dc.contributor.author	Xu, RY
dc.contributor.author	Cheng, Q
dc.date	2020-06-07
dc.date.accessioned	2021-02-18T13:01:14Z
dc.date.available	2021-02-18T13:01:14Z
dc.date.issued	2020-07-21
dc.identifier.citation	2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020, 00, pp. 1-6
dc.identifier.isbn	978-1-7281-7441-9
dc.identifier.issn	2474-9133
dc.identifier.uri	http://hdl.handle.net/10453/146212
dc.description.abstract	Device-free WiFi sensing utilizing channel state information (CSI) is attractive for human activity recognition (HAR). However, several challenging problems are yet to be resolved, e.g., difficulty in extracting proper features from input signals, susceptibility to the phase shift of CSI and difficulty in identifying similar behaviors (e.g., lying and standing). In this paper, we aim to tackle these problems by proposing a novel scheme for CSI-based HAR that uses activity filter-based deep learning network (HAR-AF-DLN) with enhanced correlation features. We first develop a novel CSI compensation and enhancement (CCE) method to compensate for the timing offset between the WiFi transmitter and receiver, enhance activity-related signals and reduce the dimension of inputs to DLN. Then, we design a novel activity filter (AF) to differentiate similar activities (e.g., standing and lying) based on the enhanced CSI correlation features obtained from CCE. Extensive simulation results demonstrate that our proposed HAR-AF-DLN scheme outperforms state-of-the-art methods with significantly improved recognition accuracy (especially for similar activities) and notably reduced training time.
dc.language	en
dc.publisher	IEEE
dc.relation	iOmniscient Pty Ltd
dc.relation.ispartof	2020 IEEE International Conference on Communications Workshops (ICC Workshops)
dc.relation.ispartof	2020 IEEE International Conference on Communications Workshops
dc.relation.isbasedon	10.1109/iccworkshops49005.2020.9145101
dc.rights	© 2020 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/embargoedAccess
dc.title	WiFi-Based Activity Recognition using Activity Filter and Enhanced Correlation with Deep Learning
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Dublin, Ireland
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	open_access	*
utslib.copyright.embargo	2022-07-21T00:00:00+1000Z
dc.date.updated	2021-02-18T13:01:03Z
pubs.finish-date	2020-06-11
pubs.publication-status	Published
pubs.start-date	2020-06-07
pubs.volume	00

Abstract:

Device-free WiFi sensing utilizing channel state information (CSI) is attractive for human activity recognition (HAR). However, several challenging problems are yet to be resolved, e.g., difficulty in extracting proper features from input signals, susceptibility to the phase shift of CSI and difficulty in identifying similar behaviors (e.g., lying and standing). In this paper, we aim to tackle these problems by proposing a novel scheme for CSI-based HAR that uses activity filter-based deep learning network (HAR-AF-DLN) with enhanced correlation features. We first develop a novel CSI compensation and enhancement (CCE) method to compensate for the timing offset between the WiFi transmitter and receiver, enhance activity-related signals and reduce the dimension of inputs to DLN. Then, we design a novel activity filter (AF) to differentiate similar activities (e.g., standing and lying) based on the enhanced CSI correlation features obtained from CCE. Extensive simulation results demonstrate that our proposed HAR-AF-DLN scheme outperforms state-of-the-art methods with significantly improved recognition accuracy (especially for similar activities) and notably reduced training time.

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http://hdl.handle.net/10453/146212