Development of Low-Cost IoT-Based Wireless Healthcare Monitoring System

Zin, M.S.I.M. and Mustafah, M.A.K. and Arith, F. and Isa, A.A.M. and Barukang, L. and Markarian, G. (2022) Development of Low-Cost IoT-Based Wireless Healthcare Monitoring System. PRZEGLĄD ELEKTROTECHNICZNY, 1 (1). pp. 222-227. ISSN 0033-2097

Abstract

According to studies, up to 99 percent of alarms triggered in hospital units are false or clinically insignificant while indicating no genuine harm to patients. However, false alarms can lead to alert overload, causing healthcare workers to miss critical occurrences that could be harmful or even fatal. The purpose of this work is to tackle the problem by developing an integrated system that can continually track the patient's health condition utilising a cloud computing platform, allowing alerts to be targeted to the appropriate medical facility personnel in a timely and orderly manner. Arduino microcontrollers are used to collect health parameters such as temperature and pulse rate and provide a real-time monitoring system for medical practitioners. Multiple sensors and an RF transceiver are attached to a small microcontroller, forming a wearable module that the patient will wear. This wearable component is wirelessly connected to the main module consisting of a larger microcontroller, where the data is then uploaded to the database in the cloud through the internet. The data can then be accessed through a web-based terminal, providing medical practitioners access through the web page. If the system detects any abrupt changes to the patient's temperature or pulse rate, a push notification will be sent to the medical practitioner's Android smartphone so that immediate action can be taken. The system is scalable as multiple wearable modules can be connected to the main module, allowing monitoring of multiple patients simultaneously. More sensors can also easily be added to the wearable module to monitor other vital health parameters such as oxygen saturation and blood pressure. The testing has indicated that the system can achieve 99.4% accuracy in temperature monitoring and 86% accuracy for pulse monitoring.