Multilocation Human Activity Recognition via MIMO-OFDM-Based Wireless Networks: An IoT-Inspired Device-Free Sensing Approach

Zhong, Y; Wang, J; Wu, S; Jiang, T; Huang, Y; Wu, Q

Multilocation Human Activity Recognition via MIMO-OFDM-Based Wireless Networks: An IoT-Inspired Device-Free Sensing Approach

Zhong, Y Wang, J Wu, S Jiang, T Huang, Y

Wu, Q

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2021, 8, (20), pp. 15148-15159
Issue Date:: 2021-10-15

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Field	Value	Language
dc.contributor.author	Zhong, Y
dc.contributor.author	Wang, J
dc.contributor.author	Wu, S
dc.contributor.author	Jiang, T
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.date.accessioned	2022-05-15T23:21:26Z
dc.date.available	2022-05-15T23:21:26Z
dc.date.issued	2021-10-15
dc.identifier.citation	IEEE Internet of Things Journal, 2021, 8, (20), pp. 15148-15159
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/157383
dc.description.abstract	Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input-multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2020.3038899
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Multilocation Human Activity Recognition via MIMO-OFDM-Based Wireless Networks: An IoT-Inspired Device-Free Sensing Approach
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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/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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-15T23:21:23Z
pubs.issue	20
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	20

Abstract:

Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input-multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.

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http://hdl.handle.net/10453/157383