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- Publisher Website: 10.1155/2019/8186573
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Article: Enabling noninvasive physical assault monitoring in smart school with commercial Wi-Fi Devices
Title | Enabling noninvasive physical assault monitoring in smart school with commercial Wi-Fi Devices |
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Authors | |
Issue Date | 2019 |
Citation | Wireless Communications and Mobile Computing, 2019, v. 2019, article no. 8186573 How to Cite? |
Abstract | Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited surveillance area, which are privacy-invasive and cannot provide ubiquitous assault monitoring. In this paper, we present Wi-Dog, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances. Wi-Dog is based on widely deployed commodity Wi-Fi infrastructures. The key intuition is that Wi-Fi signals are easily distorted by human motions, and motion-induced signals could convey informative characteristics, such as intensity, regularity, and continuity. Specifically, to explicitly reveal the substantive properties of physical assault, we innovatively propose a set of signal processing methods for informative components extraction by selecting sensitive antenna pairs and subcarriers. Then a novel signal-complexity-based segmentation method is developed as a location-independent indicator to monitor targeted movement transitions. Finally, holistic analysis is employed based on domain knowledge, and we distinguish the violence process from both local and global perspective using time-frequency features. We implement Wi-Dog on commercial Wi-Fi devices and evaluate it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog which consistently outperforms the advanced abnormal detection methods with a higher true detection rate of 94% and a lower false alarm rate of 8%. |
Persistent Identifier | http://hdl.handle.net/10722/303610 |
ISSN | 2021 Impact Factor: 2.146 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhou, Qizhen | - |
dc.contributor.author | Wu, Chenshu | - |
dc.contributor.author | Xing, Jianchun | - |
dc.contributor.author | Zhao, Shuo | - |
dc.contributor.author | Yang, Qiliang | - |
dc.date.accessioned | 2021-09-15T08:25:40Z | - |
dc.date.available | 2021-09-15T08:25:40Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Wireless Communications and Mobile Computing, 2019, v. 2019, article no. 8186573 | - |
dc.identifier.issn | 1530-8669 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303610 | - |
dc.description.abstract | Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited surveillance area, which are privacy-invasive and cannot provide ubiquitous assault monitoring. In this paper, we present Wi-Dog, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances. Wi-Dog is based on widely deployed commodity Wi-Fi infrastructures. The key intuition is that Wi-Fi signals are easily distorted by human motions, and motion-induced signals could convey informative characteristics, such as intensity, regularity, and continuity. Specifically, to explicitly reveal the substantive properties of physical assault, we innovatively propose a set of signal processing methods for informative components extraction by selecting sensitive antenna pairs and subcarriers. Then a novel signal-complexity-based segmentation method is developed as a location-independent indicator to monitor targeted movement transitions. Finally, holistic analysis is employed based on domain knowledge, and we distinguish the violence process from both local and global perspective using time-frequency features. We implement Wi-Dog on commercial Wi-Fi devices and evaluate it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog which consistently outperforms the advanced abnormal detection methods with a higher true detection rate of 94% and a lower false alarm rate of 8%. | - |
dc.language | eng | - |
dc.relation.ispartof | Wireless Communications and Mobile Computing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Enabling noninvasive physical assault monitoring in smart school with commercial Wi-Fi Devices | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1155/2019/8186573 | - |
dc.identifier.scopus | eid_2-s2.0-85065755294 | - |
dc.identifier.volume | 2019 | - |
dc.identifier.spage | article no. 8186573 | - |
dc.identifier.epage | article no. 8186573 | - |
dc.identifier.eissn | 1530-8677 | - |
dc.identifier.isi | WOS:000464820800001 | - |