Citation

Pang, Wai Leong and Chung, Gwo Chin and Chan, Kah Yoong and Lee, It Ee and Roslee, Mardeni and Fitrey, Edzham and Sim, Yee Wai and Prasetio, Murman Dwi (2023) Smart Machine Learning-based IoT Health and Cough Monitoring System. International Journal on Advanced Science, Engineering and Information Technology, 13 (5). pp. 1645-1653. ISSN 2088-5334

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Abstract

Coronavirus 2019, more commonly known as COVID-19, was declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. The β coronavirus culpable for the disease, SARS CoV-2, is known to be highly contagious with a relatively long incubation period of up to 14 days and is transmittable through small droplets, especially among people who are in close face-to-face contact. The Ministry of Health of Malaysia has recommended five days of quarantine for people who are positive for COVID-19 to avoid further disease transmission. Many resources are used to monitor patients throughout the quarantine period. Therefore, this project would like to present an IoT-enabled wearable device capable of monitoring COVID-19 quarantine patients by utilizing sensors to monitor the necessary health parameters and facilitate home quarantine. The low-cost ESP32 and Arduino Nano 33 BLE Sense microcontrollers are used in this device. They are connected to various IoT sensors to collect temperature, humidity, and sound data. The data obtained will then be uploaded to an IoT platform for doctors to analyze and monitor remotely via the health log throughout the 5-day quarantine period. An alert system is also devised to inform the medical staff if the patient is experiencing abnormal symptoms. The medical staff can then bring their attention to the patient and take the necessary actions to combat COVID-19.

Item Type:	Article
Uncontrolled Keywords:	COVID-19; wearable health monitoring system; cough detection system
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Dec 2023 03:02
Last Modified:	07 Dec 2023 03:02
URII:	http://shdl.mmu.edu.my/id/eprint/11935

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