| Author: | Ng, Chun Tan |
| Title: | Channel state information (CSI) based self quarantine monitoring system |
| Advisors: | Ho, W. H. (EIE) |
| Degree: | M.Sc. |
| Year: | 2022 |
| Subject: | Quarantine -- Data processing Self-care, Health -- Data processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electronic and Information Engineering |
| Pages: | [75] pages : color illustrations |
| Language: | English |
| Abstract: | The 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. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6526.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.63 MB | Adobe PDF | View/Open |
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