Smart Mental Health Monitoring

Abstract

Mental health disorders such as stress, anxiety, depression, and emotional imbalance have become increasingly common among students and working professionals. Early detection and continuous monitoring can help reduce severe mental health issues and improve emotional well-being. This paper presents a Mental Health Assistance and Detection (MAD) system using Internet of Things (IoT) and Machine Learning (ML) techniques. The proposed system continuously monitors physiological parameters such as heart rate and oxygen saturation using sensors connected to an ESP32 microcontroller. The collected data is analyzed using machine learning algorithms to identify abnormal stress conditions and emotional changes. The system also provides real-time alerts and assistance recommendations to users through a mobile or web interface. Experimental results show that the proposed system can effectively detect stress-related conditions with improved accuracy and real-time monitoring capabilities.

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