WiReader: Adaptive Air Handwriting Recognition Based on Commercial WiFi Signal

Guo, Z; Xiao, F; Sheng, B; Fei, H; Yu, S

WiReader: Adaptive Air Handwriting Recognition Based on Commercial WiFi Signal

Guo, Z Xiao, F Sheng, B Fei, H Yu, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2020, 7, (10), pp. 10483-10494
Issue Date:: 2020-10-01

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Field	Value	Language
dc.contributor.author	Guo, Z
dc.contributor.author	Xiao, F
dc.contributor.author	Sheng, B
dc.contributor.author	Fei, H
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-01-29T03:40:21Z
dc.date.available	2021-01-29T03:40:21Z
dc.date.issued	2020-10-01
dc.identifier.citation	IEEE Internet of Things Journal, 2020, 7, (10), pp. 10483-10494
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/145649
dc.description.abstract	© 2014 IEEE. In recent years, with the rapid development of the Internet-of-Things (IoT) technologies, many intelligent sensing applications have emerged, which realize contactless sensing and human-computer interaction (HCI). Handwriting recognition is the communication link between the human and computer. Previous handwriting recognition applications are usually founded on images and sensors, which require significant device overhead and are device dependent. Recently, the revolution of the wireless signal sensing technology has laid the foundation for the intelligent handwriting recognition technology without devices. In this article, we propose WiReader, an adaptive air handwriting recognition system based on wireless signals. WiReader utilizes ubiquitous commercial WiFi devices to process the collected channel state information (CSI), segments the data in combination with activity factors, and then transforms the original signal using the CSI-Ratio model. In order to address the problem of feature extraction caused by handwriting, we utilize the cumulative principal components and multilayer wavelet transform for the transformed signal. Finally, the energy feature matrix is generated and combines with long short-term memory (LSTM) to realize the recognition of different handwriting actions. Extensive real-world experiments show that WiReader achieves an average recognition accuracy of 90.64% leading other applications in three scenarios and has strong robustness to user location, user diversity, and different scenarios.
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.2020.2997053
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/restrictedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	WiReader: Adaptive Air Handwriting Recognition Based on Commercial WiFi Signal
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-29T03:40:17Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	10

Abstract:

© 2014 IEEE. In recent years, with the rapid development of the Internet-of-Things (IoT) technologies, many intelligent sensing applications have emerged, which realize contactless sensing and human-computer interaction (HCI). Handwriting recognition is the communication link between the human and computer. Previous handwriting recognition applications are usually founded on images and sensors, which require significant device overhead and are device dependent. Recently, the revolution of the wireless signal sensing technology has laid the foundation for the intelligent handwriting recognition technology without devices. In this article, we propose WiReader, an adaptive air handwriting recognition system based on wireless signals. WiReader utilizes ubiquitous commercial WiFi devices to process the collected channel state information (CSI), segments the data in combination with activity factors, and then transforms the original signal using the CSI-Ratio model. In order to address the problem of feature extraction caused by handwriting, we utilize the cumulative principal components and multilayer wavelet transform for the transformed signal. Finally, the energy feature matrix is generated and combines with long short-term memory (LSTM) to realize the recognition of different handwriting actions. Extensive real-world experiments show that WiReader achieves an average recognition accuracy of 90.64% leading other applications in three scenarios and has strong robustness to user location, user diversity, and different scenarios.

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http://hdl.handle.net/10453/145649