— In today’s fast-paced world, individuals often struggle to recognize and manage their emotions in real time, creating a growing demand for intelligent, accessible mental health support systems. Mental health issues such as anxiety, depression, and stress are increasingly prevalent, yet support systems remain scarce. While AI has advanced in facial recognition and text sentiment analysis, few approaches integrate these modalities with evidence-based psychological interventions. We present EmotionalWell AI, a hybrid framework combining DeepFace-based facial analysis and text emotion classification with CBT and DBT-informed recommendations. Unlike prior diagnostic-only systems, EmotionalWell AI delivers actionable wellness feedback via an interactive Streamlit application, logging multimodal inputs, analyzing trends, and providing personalized coping strategies. Preliminary evaluations demonstrate improved accuracy over unimodal models, and user feedback confirms its potential as a self-help companion for emotional regulation. This work bridges AI-driven detection with psychological intervention, offering a step toward next-generation digital mental health tools.Keywords—Multimodal emotion detection; Artificial intelligence in psychology; CBT; DBT; Human-computer interaction; Digital mental health; Hybrid AI systems.
Ayushi Mittal (Tue,) studied this question.
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