Citation

Pang, Wai Leong and Chung, Gwo Chin and Chan, Kah Yoong and Sim, Yee Wai and ., Muhammad Akmal and Prasetio, Murman Dwi (2024) Design of Smart IoT Health and Social Distancing Monitoring System. International Journal of Integrated Engineering, 16 (3). pp. 36-44. ISSN 2229-838X

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Abstract

Since the outbreak of the COVID-19 pandemic, many companies have started to work from home. As the pandemic recovers, companies slowly start to adapt to the situation by making the workers work from the office. This is an effort to reduce the risks of spreading diseases in the workplace during the endemic of COVID-19. An effective solution is urgently needed to reduce and control the transmission rate of COVID-19. This has motivated us to design an Internet of Things (IoT) Health and Social Distancing Monitoring System (IHDS). This system aims to support that initiative and introduce a system that can control the risks of infection among workers due to a worrying spike in the number of cases in the workplace. The proposed system monitors the health condition of the users and controls social distancing at the workplace by using IoT technology and machine learning. Extensive experiments were conducted to assess the performance of the proposed system. Four critical health metrics were closely monitored: body temperature, pulse rate, blood oxygen saturation, and cough detection, achieving impressive accuracy rates of 99.91%, 94.32%, 99%, and 80.5%, respectively. The proposed system initiates the assignment of red boxes to couples who are separated by less than 1 meter, while it designates green boxes for couples who maintain more than a 1-meter distance from each other

Item Type:	Article
Uncontrolled Keywords:	IoT, social distancing monitoring, machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 May 2024 07:16
Last Modified:	31 May 2024 07:16
URII:	http://shdl.mmu.edu.my/id/eprint/12510

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