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Public health and safety on close contact proximity detection for COVID-19 and alert via IoT

Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth sig...

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Main Authors: Nur Athirah Mohd Noor,, Zainal Hisham Che Soh,, Mohamad Nizam Ibrahim,, Mohd Hanapiah Abdullah,, Siti Noraini Sulaiman,, Irni Hamiza Hamzah,, Syahrul Afzal Che Abdullah,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2023
Online Access:http://journalarticle.ukm.my/22753/1/07.pdf
http://journalarticle.ukm.my/22753/
https://www.ukm.my/jkukm/volume-3504-2023/
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spelling my-ukm.journal.227532023-12-29T06:35:21Z http://journalarticle.ukm.my/22753/ Public health and safety on close contact proximity detection for COVID-19 and alert via IoT Nur Athirah Mohd Noor, Zainal Hisham Che Soh, Mohamad Nizam Ibrahim, Mohd Hanapiah Abdullah, Siti Noraini Sulaiman, Irni Hamiza Hamzah, Syahrul Afzal Che Abdullah, Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone user in their surrounding. The system aims to alert user if the social distancing is breached. The methodology rely on the Radio Signal Strength Indicator (RSSI) signal to analyze and estimate the proximity distance and duration of the individual’s exposure to other peoples in surrounding area. An overlap zone of 1-meter is used to indicate detection of closed contact proximity between users. Furthermore, the collected data can be utilized for contact tracing that enabling health officials to identify the closed contact of infected patient systematically and rapidly covering people who may be anonymous or not directly known to the COVID-19 patient. Encouraging results have been obtained for the closed contact proximity detection within the mobile apps. Furthermore, the performance of system for close contact proximity detection has shown that indoor locations have a more robust signal distribution compared to outdoor locations Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22753/1/07.pdf Nur Athirah Mohd Noor, and Zainal Hisham Che Soh, and Mohamad Nizam Ibrahim, and Mohd Hanapiah Abdullah, and Siti Noraini Sulaiman, and Irni Hamiza Hamzah, and Syahrul Afzal Che Abdullah, (2023) Public health and safety on close contact proximity detection for COVID-19 and alert via IoT. Jurnal Kejuruteraan, 35 (4). pp. 849-855. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3504-2023/
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone user in their surrounding. The system aims to alert user if the social distancing is breached. The methodology rely on the Radio Signal Strength Indicator (RSSI) signal to analyze and estimate the proximity distance and duration of the individual’s exposure to other peoples in surrounding area. An overlap zone of 1-meter is used to indicate detection of closed contact proximity between users. Furthermore, the collected data can be utilized for contact tracing that enabling health officials to identify the closed contact of infected patient systematically and rapidly covering people who may be anonymous or not directly known to the COVID-19 patient. Encouraging results have been obtained for the closed contact proximity detection within the mobile apps. Furthermore, the performance of system for close contact proximity detection has shown that indoor locations have a more robust signal distribution compared to outdoor locations
format Article
author Nur Athirah Mohd Noor,
Zainal Hisham Che Soh,
Mohamad Nizam Ibrahim,
Mohd Hanapiah Abdullah,
Siti Noraini Sulaiman,
Irni Hamiza Hamzah,
Syahrul Afzal Che Abdullah,
spellingShingle Nur Athirah Mohd Noor,
Zainal Hisham Che Soh,
Mohamad Nizam Ibrahim,
Mohd Hanapiah Abdullah,
Siti Noraini Sulaiman,
Irni Hamiza Hamzah,
Syahrul Afzal Che Abdullah,
Public health and safety on close contact proximity detection for COVID-19 and alert via IoT
author_facet Nur Athirah Mohd Noor,
Zainal Hisham Che Soh,
Mohamad Nizam Ibrahim,
Mohd Hanapiah Abdullah,
Siti Noraini Sulaiman,
Irni Hamiza Hamzah,
Syahrul Afzal Che Abdullah,
author_sort Nur Athirah Mohd Noor,
title Public health and safety on close contact proximity detection for COVID-19 and alert via IoT
title_short Public health and safety on close contact proximity detection for COVID-19 and alert via IoT
title_full Public health and safety on close contact proximity detection for COVID-19 and alert via IoT
title_fullStr Public health and safety on close contact proximity detection for COVID-19 and alert via IoT
title_full_unstemmed Public health and safety on close contact proximity detection for COVID-19 and alert via IoT
title_sort public health and safety on close contact proximity detection for covid-19 and alert via iot
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2023
url http://journalarticle.ukm.my/22753/1/07.pdf
http://journalarticle.ukm.my/22753/
https://www.ukm.my/jkukm/volume-3504-2023/
_version_ 1787134662255050752
score 13.211869

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