New normal: cooperative paradigm for COVID-19 timely detection and containment using Internet of things and deep learning
- Authors
- Kumbhar, Farooque Hassan; Hassan, Syed Ali; Shin, Soo Young
- Issue Date
- Oct-2021
- Publisher
- TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY
- Keywords
- Convolution neural network; contagious diseases; internet of things; smart city; tracking
- Citation
- TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, v.29, pp.2795 - 2806
- Journal Title
- TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
- Volume
- 29
- Start Page
- 2795
- End Page
- 2806
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20285
- DOI
- 10.3906/elk-2105-96
- ISSN
- 1300-0632
- Abstract
- The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things (IoT) based paradigm using convolution neural networks (CNN), smart wearable, and connected E-Health to help governments resume normal life again. Our autonomous scheme provides three-level detection: inter-object distance for social distancing violations using CNN, area-based monitoring of active smartphone users and their current state of illness using connected E-Health, and smart wearable. Our exhaustive performance evaluation identifies that the proposed scheme with CNN YOLOv3 achieves up to 90% mean average precision in detecting social distancing violations, as compared to existing YOLOv2 achieving 70% and faster R-CNN with 76%. Our evaluation also identifies that wearing protective gear reduces spread by 50%, and timely actions in the first hour can help avoid three times COVID-19 exposure.
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Collections - School of Electronic Engineering > 1. Journal Articles
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