IoT enabled health indicators estimation and indoor environment classification

Internet of Things (IoT) and Machine Learning (ML) have revolutionized the way we approach monitoring and analysing physiological data. Through these technologies invaluable insights can be gathered for early detection of cardiovascular issues, optimizing exercise routines or predicting stress levels. This study presents the development of an IoT test-bed, utilizing a single-board computer alongside ambient environment and health sensors for data collection. A data analysis pipeline has been designed to accurately estimate Heart Rate (HR) and Skin Resistance (SR) values exclusively using the ambient environment data and to classify the environment according to the risk it poses on cardiac health. The results of this study indicate the potential of using ML to capture the relationships between ambient environment conditions and health indicators. It has been found that Random Forest (RF) models are capable of classifying environments in three risk categories with an accuracy of 86.5% and estimate HR and SR with a MAE of 1.86 and 0.36, respectively. These contributions collectively advance the understanding of how environmental factors such as temperature, humidity, pressure and air quality influence health and show a promising potential for non-invasive monitoring.