EmoMA-Net: A Novel Model for Emotion Recognition Using Hybrid Multimodal Neural Networks in Adaptive Educational Systems

This study presents an Emotion Recognition Multi-Attention Model (EmoMA-Net), a novel multimodal neural network aimed at enhancing real-time emotion recognition in educational environments. By leveraging the WESAD dataset, our model combines Convolutional Neural Networks (CNN), a Time Series Memory System (TSMS), and a Multi-Attention Mechanism to analyze diverse physiological signals, such as heart rate variability (HRV) and electroencephalogram (EEG). Unlike traditional emotion recognition methods reliant on subjective self-reports, our model delivers objective and accurate predictions of student stress levels through multimodal physiological data collected from wearable sensors. Achieving accuracy up to 99.66%, it facilitates adaptive educational systems to provide real-time feedback to educators, enabling prompt adjustments to teaching strategies. This advancement represents forward in emotion prediction technology, contributing to more responsive and adaptive educational experiences based on real-time emotional insights.