Integrating Beck's Depression Inventory and Emotion Detection Using GAN for Comprehensive Depression Assessment and Intervention

This research study suggests a novel approach to measure depression levels by integrating emotion recognition using Generative Adversarial Networks (GAN) and Beck's Depression Inventory (BDI). The challenge encountered here, was that relying solely on facial recognition could lead to inaccurate results, as individuals may manipulate their expressions. To address this, we integrated BDI score assessment to supplement emotion detection. Additionally, we faced the challenge of ensuring the authenticity of facial recognition amidst potential manipulation. Therefore, we implemented face recognition for each BDI question, ensuring accurate emotion detection by the completion of the questionnaire. Our technique combines state-of-the-art AI-driven emotion analysis with the widely used BDI psychological evaluation tool to offer a comprehensive and multimodal approach in diagnosing depression. This method provides a more complete image of the person's emotional state by analyzing self-reported BDI scores and emotional data produced by the GAN model. A thorough and accurate assessment of the degree of depression is produced when these tests are combined. Further, the system provides customized activities and intervention strategies for people with varying degrees of distress based on the determined depression level. This study represents a significant advancement in the use of technology for extensive mental health screening and treatment.