Internet of Things Based COVID-19 Patient in Self-Isolation Monitoring System

Abstract

Self-isolation has become a critical measure for managing symptomatic or asymptomatic COVID-19 patients, particularly when healthcare facilities are overwhelmed. This study explores a monitoring system based on commercially available Wear OS smartwatches designed to track patient vital signs, specifically oxygen saturation (SpO₂), heart rate, and location, and generate alerts during atypical events. The research has two primary objectives, first, to propose a method for remote patient monitoring using market-ready wearable devices; and second, to implement a fuzzy logic model in MATLAB that analyzes vital sign data, including heart rate, SpO₂, body temperature, systolic and diastolic blood pressure, and respiratory rate, for health assessment. The findings demonstrate that the system successfully acquires data from the wearable device and processes it through the fuzzy logic engine. This model can effectively categorize patient status as healthy, requiring warning, or in an emergency based on the integrated vital sign inputs.