An intelligent stress detection and monitoring system using the IoT environment

This study presents an IoT -based intelligent medical stress system to help doctors remotely monitor their patients stress status. This system is made up of a medical kit measuring the galvanic skin response, the oxygen saturation and the heart rate thanks to sensors attached at the patient's arm which send the measured physiological values to the Firebase server. These values are retrieved and processed by the Machine Learning SVM algorithm which deduces, therefrom, whether the patient is stressed or not. A mobile application is then used to monitor in real-time the patient physiological values and his stress state by visualizing a complete dashboard. To achieve this objective, we used a NodeMcu board connected with a MAX30100 (measuring pulse oximetry and heart rate signals) and GSR sensors, a Raspberry Pi board for the Artificial Intelligence (AI) process, Ionic framework to develop the mobile application and FireBase as a cloud to save data.