IoT-Based Health Monitoring System

Hee, Yan Feng (2026) IoT-Based Health Monitoring System. Final Year Project (Diploma), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Healthcare is important no matter who you are even if you are a doctor. Some of us only know little to no knowledge about our own health and don’t even bother to pay a doctor a visit for a checkup or even think about a health monitoring system. As a result, they get incurable sickness or maybe a sudden life threating situation out of the blue like heart attack due to their neglection to visit a doctor or doesn’t have the ability to purchase a health monitoring system due to how expensive they are. The lack of reliable remote health monitoring systems creates a significant barrier to proactive healthcare, potentially leading to undiagnosed conditions and critical health emergencies for decentralized patients. This project addresses this gap by designing and developing a low-cost, IoT-based health monitoring system utilizing an ESP32 microcontroller as its core processing unit, chosen for its integrated Wi-Fi capability and its dual-core processing power. It acquires physiological data through a suite of sensors which are the MAX30100 module for heart rate and blood oxygen saturation (SpO2), the DHT11 for body temperature, and the AD8232 for electrocardiogram (ECG signals. The analog ouputs from sensors like the AD8232 are connected using their built-in or the ESP32’s internal Analog-to-Digital Converters (ADCs) to be processed digitally. The processed data is displayed locally on an I2C LCD for user feedback and is transmitted wirelessly via the ESP32’s integrated Wi-Fi module to the Blynk IoT cloud platform. This enables real-time remote monitoring through a customized mobile application dashboard. During the testing phase, the system's accuracy was verified by comparing the prototype's readings against commercial pulse oximeters and commercial digital thermometers showing a highly correlated biometric result. Moreover, the system’s reliability was significantly optimized through empirical analysis. For example, in my system, false biometric triggers caused by ambient light interference were successfully eliminated using an infrared threshold logic, and volatile data streams were stabilized via mathematical moving-average buffers. Moreover, a power consumption analysis of the active hardware which includes the ESP32 microcontrollers, biometric sensors, local displays, and implementing CPU frequency downscaling with automated hardware sleep modes significantly reduced the overall current draw. This energy optimization effectively prolongs the battery life which proves the prototype's viability for continuous, portable wearable use. By providing continuous health insights, this system not only enhances user’s safety but also reduces the burden on caregivers, offering a practical and cost-effective solution for in-home health monitoring.