IoT-Blood Pressure Monitoring System Based on Photoplethysmogram (PPG) and Electrocardiogram (ECG) Sensor

Abstract

In order to effectively monitor and manage hypertension, which is still a major global health concern, sophisticated solutions are required. In order to estimate blood pressure non-invasively, this study presents a novel, cuffless blood pressure monitoring system that makes use of photoplethysmogram (PPG) and electrocardiogram (ECG) technology. The system makes use of important hardware elements including the Arduino Uno platform, the AD8232 ECG sensor, and a pulse sensor by investigating unusual electrode locations for ECG and combining PPG signals. MATLAB is used for the comprehensive analysis of both the diastolic and systolic signals. The technique entails obtaining signals from PPG and ECG sensors, pre-processing them, and then extracting important characteristics such as the blood pressure-correlated Pulse Transit Time (PTT). MATLAB is used for data analysis in order to create algorithms for continuous BP estimation. When the system is verified against standard sphygmomanometers, the results demonstrate that its performance is comparable to that of conventional blood pressure monitoring devices, with average deviations of SBP and DBP falling within clinically acceptable ranges. Additionally, the system is connected to IoT platforms like ThingSpeak and ThingView and is built for real-time health monitoring and data analysis. This offers continuous monitoring capabilities and remote accessibility, which are critical for preventive hypertension control. This study has successfully demonstrated that cuffless blood pressure monitoring is possible and has improved the management of hypertension by addressing the shortcomings of traditional cuff-based systems, such as discomfort and a lack of continuous monitoring.