New normal: Cooperative paradigm for covid-19 timely detection and containment using internet of things and deep learning
Turkish Journal of Electrical Engineering and Computer Sciences
; 29:2795-2806, 2021.
Article
in English
| Scopus | ID: covidwho-1481064
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. © 2021 Turkiye Klinikleri. All rights reserved.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Turkish Journal of Electrical Engineering and Computer Sciences
Year:
2021
Document Type:
Article
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