IoT-BASED LONG COVID DEVICE & MONITORING SYSTEM

Abstract

IoT-based health monitoring solutions have the potential to be extremely valuable for COVID-19 patients throughout the epidemic. This research provides an IoT-based system for real-time health monitoring that employs measured values of a patient's body temperature, pulse rate, and oxygen saturation, which are the most significant critical care indicators. The device has a liquid crystal display (LCD) that shows the current temperature, heart rate, and oxygen saturation level, and it can be readily synced with a mobile app for quick access. Among the commercially available Arduino boards are the Uno, Due, Mega, and Leonardo. The Arduino Uno features 20 I/O pins, 14 digital and 6 Analogue. The Arduino Due is equipped with 54 digital I/O pins, 12 analogue I/O pins, and two analogue output pins. The Arduino Mega has 54 digital I/O pins, 16 analogue inputs, and no output pins. The Arduino Leonardo has 20 digital I/O pins, 12 analogue input pins, and no output pins. Because of its pin configuration, I chose the Arduino Uno as the system's main controller. It is a well-known opensource microcontroller board based on the ATmega328p. The proposed Internet of Things technique is based on an Arduino Uno system and has been tested and validated on five human testers. To diagnose patients remotely, the user must pair the device with their phone and use the apps. The data will be saved in the Apps. The system's results are promising: the data it collects is stored at lightning speed. The system's results are determined to be accurate when compared to other commercially available equipment. During the COVID-19 epidemic, IoT-based solutions might be extremely useful in saving lives. IoT-based collected real-time GPS assists in automatically alerting the patient to reduce risk factors. Wearable IoT devices are attached to the human body and networked with edge nodes to analyse data and make health-related decisions. This system leverages the wearable IoT sensor, cloud, and web layers to examine the patient’s health condition remotely. The first layer gathers patient health information, which is then transferred to the second layer, which stores it in the cloud. The network analyses health data and notifies patients, allowing users to act quickly.