IoT-based Mental Health Monitoring with Machine Learning

In this paper, we focus on an IoT-Based Mental Health Monitoring System, which integrates wearable sensors with machine learning to provide real-time monitoring of stress and anxiety. The system consists of sensors to monitor heart rates connected to microcontroller (ESP32) and to provide continuous data regarding the physiological conditions of the user. A data pipeline thus allows the smooth flow of monitored data into mobile phones or a cloud environment. Machine learning models applied include SVM and Neural Networks to achieve high accuracy in mental health condition analysis and prediction. The mobile platform application of this system can thus serve as an easy-to-use mental health management tool where actionable insights, alerts, and personalized recommendations are provided. This study highlights the ability to completely transform mental health care systems into anticipatory care and timely interventions.