Open Access

ARTICLE

Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19

Hameed AlQaheri^1,*, Manash Sarkar², Saptarshi Gupta³, Bhavya Gaur⁴

1 Department of Information Systems and Operation Management, College of Business Administration, Kuwait University, 13060, Kuwait
2 Atria Institute of Technology, Bengaluru, Karnataka, India
3 Electronics and Communication Engineering, SRM Institute of Science & Technology, Delhi-NCR, 201204, India
4 Business Technology Analyst, ZS Associates, Pune, 411014, India

* Corresponding Author: Hameed AlQaheri. Email:

Computers, Materials & Continua 2022, 72(1), 497-517. https://doi.org/10.32604/cmc.2022.022735

Received 17 August 2021; Accepted 18 October 2021; Issue published 24 February 2022

Abstract

The most common alarming and dangerous disease in the world today is the coronavirus disease 2019 (COVID-19). The coronavirus is perceived as a group of coronaviruses which causes mild to severe respiratory diseases among human beings. The infection is spread by aerosols emitted from infected individuals during talking, sneezing, and coughing. Furthermore, infection can occur by touching a contaminated surface followed by transfer of the viral load to the face. Transmission may occur through aerosols that stay suspended in the air for extended periods of time in enclosed spaces. To stop the spread of the pandemic, it is crucial to isolate infected patients in quarantine houses. Government health organizations faced a lack of quarantine houses and medical test facilities at the first level of testing by the proposed model. If any serious condition is observed at the first level testing, then patients should be recommended to be hospitalized. In this study, an IoT-enabled smart monitoring system is proposed to detect COVID-19 positive patients and monitor them during their home quarantine. The Internet of Medical Things (IoMT), known as healthcare IoT, is employed as the foundation of the proposed model. The least-squares (LS) method was applied to estimate the linear model parameters for a sequential pilot survey. A statistical sequential analysis is performed as a pilot survey to efficiently collect preliminary data for an extensive survey of COVID-19 positive cases. The Bayesian approach is used, based on the assumption of the random variable for the priori distribution of the data sample. Fuzzy inference is used to construct different rules based on the basic symptoms of COVID-19 patients to make an expert decision to detect COVID-19 positive cases. Finally, the performance of the proposed model was determined by applying a four-fold cross-validation technique.

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