IOT COVID-19 Portable Health Monitoring System Using Raspberry Pi, Node-Red and ThingSpeak

• Published in: IEEE Symposium on Wireless Technology and Applications, ISWTA
• Main Author: Kamarozaman N.B.; Awang A.H.
• Format: Conference paper
• Language: English
• Published: IEEE Computer Society 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125709795&doi=10.1109%2fISWTA52208.2021.9587444&partnerID=40&md5=68eeaa31b7d0a9e98878aeb7e6fbe233

This project presents a reasonable budget system, designed to help doctors and guardians to monitor the wellness and health condition of their patients even from a distance. This is very important during COVID-19, where there is a need to keep a distance from people. The current monitoring systems at the hospital are mainly wired to bulk equipment for monitoring. There are also monitoring devices that can be used at home. They also have the restriction of movement towards the patient as the devices can only be applied at the bedside of the patient as they are connected by wires to the monitor. As the patient is restricted with their mobility, this interferes with their daily routine and the doctors and nurses at the hospital will have a hard time in keeping updates with the patient's condition. This paper presents an Internet of Things (IoT) based portable patient monitoring system that can be fixed to the remote patient without requiring the patient to be constrained by wires and remain at bed for the whole day. Furthermore, the device can measure and display the health status of a patient which is vital and needed for a better healthcare. Raspberry Pi is used as the central controller for the monitoring system. Sensors sent data through Wi-Fi that is integrated in the Raspberry Pi platform to the database. Similarly, for each of the sensors connected which are LM35 temperature sensor, AD8232 ECG Sensor, MAX30100 pulse oximeter sensor used MQTT server protocol to transmit the data to Node-Red and ThingSpeak for monitoring. The ThingSpeak displays real-time sensors' data and can be monitored on a webpage. An alarm is also sent through ThingTweet if needed. The obtained data from the sensors are saved on a database web page for offline smart pattern analysis in the future for the patient. © IEEE 2021