Open Access

ARTICLE

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

M. Jayalakshmi¹, Lalit Garg^2,*, K. Maharajan³, K. Jayakumar⁴, Kathiravan Srinivasan⁵, Ali Kashif Bashir⁶, K. Ramesh⁷

1 National Engineering College, Kovilpatti, 628503, India
2 Faculty of Information and Communication Technology, University of Malta, Msida, MSD2080, Malta
3 Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology, Chennai, 600062, India
4 School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India
5 School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India
6 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
7 Department of Computer Science, Karnataka State Akkamahadevi Women’s University, Vijayapura, India

* Corresponding Author: Lalit Garg. Email:

Computers, Materials & Continua 2021, 67(2), 2431-2447. https://doi.org/10.32604/cmc.2021.015352

Received 17 November 2020; Accepted 21 December 2020; Issue published 05 February 2021

Abstract

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

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Citations