AI-Based Multimodal Mental Health Monitoring for Stress Detection in Students

Students frequently deal with stress, which has a negative effect on both their mental and academic health. The intricacy of stress indicators is not adequately captured by traditional stress detection techniques, which frequently rely on self-reports or unidimensional data. We suggest an AI-based system that uses Convolutional Neural Networks (CNNs) to analyze multi modal data in order to close this gap. Our method combines textual data, including sentiment analysis, behavioural indicators, such as voice tone and facial expressions, and physiological indications, such as skin temperature and heart rate. Compared to more conventional algorithms like Random Forest, the system's use of CNNs allows it to identify stress patterns with more precision. With an accuracy of 92%, the CNN-based model greatly surpassed the Random Forest's 78%. It also showed gains in other important measures, such as precision and recall. This thorough analysis makes it easier to identify stress early on and provide prompt treatments for students' improved mental health management. The study lays the groundwork for future real-time, adaptable solutions in educational contexts and highlights the potential of deep learning to improve stress monitoring systems.