Full metadata record
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.contributor.advisorHo, W. H. (EIE)en_US
dc.creatorNg, Chun Tan-
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleChannel state information (CSI) based self quarantine monitoring systemen_US
dcterms.abstractThe daily life of people around the world has changed dramatically in recent years due to the impact of COVID-19, and work related to disease prevention is currently of the utmost importance. Hong Kong has experienced five outbreak periods during the last two years, with the fifth outbreak breaking through almost the entire healthcare system in Hong Kong, resulting in many patients having to stay at home in self-quarantine without medical supervision. To solve the problem of insufficient manpower for quarantine monitoring and management, this report proposes a CSI-based Self-Quarantine Monitoring System that requires only the current wireless signal with high indoor coverage for unmanned quarantine monitoring. The system includes indoor crowd counting, and fall detection functions and can meet the needs of automatic monitoring of patients in hotels or home isolation without violating their privacy. The system proposes the Multi-task Vision Transformer (MtViT) model, a deep learning model that can support multiple tasks while ensuring high recognition accuracy. MtViT can perform individual predictions of multiple tasks and combine the results of multiple tasks to fit the prediction and monitoring needs of different environmental situations. Finally, the CSI-based Self-Quarantine Monitoring System achieves an accuracy of over 99.1% for each task evaluated in the laboratory and an F1-score of over 0.98 for all predictions.en_US
dcterms.extent[75] pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHQuarantine -- Data processingen_US
dcterms.LCSHSelf-care, Health -- Data processingen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

Files in This Item:
File Description SizeFormat 
6526.pdfFor All Users (off-campus access for PolyU Staff & Students only)3.63 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show simple item record

Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/12067