An AI-Driven Multimodal Framework for Depression Screening Using  Conversational Analysis and Facial Emotion Recognition

Dr.A.Nagarjuna Reddy,Dr.P.Suneel Kumar,Dr. P.Avinash,Dr Md Nazmoddin

An AI-Driven Multimodal Framework for Depression Screening Using Conversational Analysis and Facial Emotion Recognition

Authors

Dr.A.Nagarjuna Reddy,Dr.P.Suneel Kumar,Dr. P.Avinash,Dr Md Nazmoddin

Keywords:

Depression Detection, Artificial Intelligence, Chatbot Systems, Natural Language Processing (NLP), Facial Emotion Recognition, Multimodal Learning, Deep Learning, Convolutional Neural Networks (CNN), Sentiment Analysis, Real-Time Monitoring, Mental Health Assessment, Human Computer Interaction, Emotion Classification, Feature Fusion, Attention Mechanism, Behavioral Analysis, Computer Vision, Healthcare AI, Affective Computing, Intelligent Diagnostic Systems

Abstract

The proposed RT-DepDetect algorithm introduces amultimodal AI framework for real-time depression detectionby integrating chatbot-based conversational analysis withlive facial emotion recognition. Initially, user interaction is captured through an intelligent chatbot that processes textualresponses using natural language processing (NLP

References

[1] M. Kowalewski, M. Stroinski, K. Kwarciak, V. Laptiev, and D. Hemmerling, "End-to-end multimodal system for

depression detection from online recordings," in 2023 45th Annual International Conference of the IEEE Engineering inMedicine & Biology Society (EMBC), Sydney, Australia, 2023, pp. 1-4, doi: 10.1109/EMBC40787.2023.10340782.

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Published

2026-07-15

How to Cite

Dr.A.Nagarjuna Reddy,Dr.P.Suneel Kumar,Dr. P.Avinash,Dr Md Nazmoddin. (2026). An AI-Driven Multimodal Framework for Depression Screening Using Conversational Analysis and Facial Emotion Recognition. Journal of Computational Analysis and Applications (JoCAAA), 35(7), 255–2665. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5893