Browsing by Author "PRETO, PEDRO MIGUEL PERES"
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- Ai-driven emotion recognition for mental health diagnoses: Assessing mental health through emotional state evaluationPublication . PRETO, PEDRO MIGUEL PERES; Conceição, Luís Manuel Silva; Figueiredo, Ana Maria Neves Almeida BaptistaMental health conditions remain a concerning challenge across the globe, requiring timely and reliable approaches to correctly make accurate diagnoses and effective interventions. Traditional assessment methods often rely on subjective self-reports and clinical interviews, which may not always capture the full spectrum of an individual’s emotional state. In this context, computational techniques for emotion analysis provide a complementary perspective by identifying patterns in facial expressions, speech, and language. This dissertation evaluates the potential of multimodal emotional state analysis and its contribution to mental health assessment, through the development of a computational application. A systematic review was conducted to evaluate existing methodologies and highlight their strengths, limitations, and applicability in clinical contexts. Building on this review, the present work explores an integration of visual, vocal, textual patterns, assessing the contribution of their combined capacity to improve the consistency and depth of emotional interpretation. An analysis centered on methodological design was conducted by applying techniques such as preprocessing, fine-tuning, and data augmentation on the datasets to enhance the model’s capacity. Ethical and security considerations were also incorporated to strengthen system robustness and ensure responsible deployment in the market. The proposed solution consists of an artificial intelligence based multimodal system that integrates the analysis of emotions present in facial expressions, voice, and text patterns to provide a comprehensive assessment of the user’s emotional state. The application’s modular architecture enables real-time processing and the generation of clinical reports. The experimental validation of the system revealed promising results across several DSM-5 domains, the clinical reference manual that defines diagnostic criteria for mental disorders cases. High F1-scores were recorded in domains such as Anger (0.84) and Personality Functioning (0.87), while more subtle domains, such as Dissociation (0.43) and Repetitive Behaviors (0.52), revealed more modest performance. The overall analysis resulted in an observed agreement level of 71.9% and a Cohen’s Kappa of 0.42, indicating moderate agreement with the DSM-5. The findings underline the promise of computational emotion analysis as a supplementary tool for mental health professionals, while also emphasizing the importance of critical evaluation of its limitations and careful integration into clinical practice.