Depression Detection Using Multimodal Analysis with Chatbot Support

Depression, a widespread psychiatric disorder affecting people globally, spans all age groups, predominantly impacting adults. This bipolar disorder characterized by symptoms including pessimism, hopelessness, anhedonia, and sadness, significantly influences lives, contributing to depression. Our paper proposes a multi-model approach for depression detection, utilizing facial expression analysis, audio evaluation, and user text input through deep learning algorithms, alongside an intelligent chatbot for personalized support. This hybrid model integrates facial expressions, audio features, and textual input for a comprehensive approach to depression detection. The methodology includes four key objectives: a CNN model for real-time or pre-recorded video facial expression analysis, audio evaluation using an NLP algorithm to transcribe users' voices, text-based analysis uncovering linguistic patterns and emotional context, and Multimodal Fusion integrating outputs for a unified multimodal approach. The intelligent chatbot encourages users to share emotions openly, enhancing the system's accuracy in identifying individuals at risk of depression. Results demonstrate the fusion's contribution to early depression detection, enabling timely interventions and improving accuracy, efficiency, and overall performance.