INTEGRATED ENVIRONMENTAL AND PHYSIOLOGICAL MONITORING FOR CARDIOVASCULAR RISK DETECTION USING IOT AND MACHINE LEARNING

Abstract

This study investigates the impact of air pollution on heart rate variability (HRV), a key physiological marker reflecting the state of the autonomic nervous and cardiovascular systems. Despite growing interest, the complex relationship between environmental exposure and HRV, especially in the context of early cardiovascular disease (CVD) detection, remains insufficiently explored. An integrated real-time monitoring system was developed using Internet of Things (IoT) devices and machine learning (ML) methods to collect and analyze data from 10 healthy participants (aged 18–22) in three different environments: a controlled laboratory, an urban roadside (Al-Farabi Avenue), and a natural setting (botanical garden). Physiological signals (RMSSD, SDNN, LF, HF) were obtained using Polar H10 ECG sensors and Zhurek PPG devices, while environmental data (PM2.5, PM10, CO₂) were recorded via Tynys and Qingping sensors. Three supervised ML models—deep neural networks (DNN), random forest (RF), and XGBoost—were used to classify HRV levels based on environmental parameters. Among them, XGBoost achieved the best performance with 91.92% accuracy, 91.82% precision, and a 90.42% F1-score. The results revealed a consistent negative correlation between higher levels of PM2.5 and PM10 and reduced HRV metrics, particularly SDNN and RMSSD, indicating potential autonomic dysfunction and increased cardiovascular risk. Although CO₂ levels showed weaker associations, their influence was still noted. These findings emphasize the importance of considering environmental factors in health monitoring and demonstrate the potential of IoT and ML technologies in enabling early detection of cardiovascular stress and supporting personalized healthcare strategies.