Multi-Modal Emotion Detection and Tracking System Using AI Techniques

Emotion detection significantly impacts healthcare by enabling personalized patient care and improving treatment outcomes. Single-modality emotion recognition often lacks reliability due to the complexity and subjectivity of human emotions. This study proposes a multi-modal emotion detection platform integrating visual, audio, and heart rate data using AI techniques, including convolutional neural networks and support vector machines. The system outperformed single-modality approaches, demonstrating enhanced accuracy and robustness. This improvement underscores the value of multi-modal AI in emotion detection, offering potential benefits across healthcare, education, and human–computer interaction.