Browsing by Author "SILVA, OSVALDO DANIEL MARTINS VIEIRA DA"
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- A machine learning approach to affective computing in the metaversePublication . SILVA, OSVALDO DANIEL MARTINS VIEIRA DA; Pereira, Ivo André SoaresIntegrating Affective Computing in the Metaverse presents revolutionising opportunities in virtual interactions by enabling emotionally intelligent systems that can recognize, interpret and respond to human emotions. This study explores the development of a Machine Learning model for accurate and reliable affect recognition for future deployment in the Metaverse and likewise environments, while also exploring the potential of implementing such systems in order to enhance user engagement, user well-being and general interaction quality in virtual environments. The state of the art in the interdisciplinary field of Affective Computing and its potential to be applied in the Metaverse was explored, while investigating the impacts this implementation could have and also taking into account the ethical concerns that rise from dealing with personal, thus sensitive, data and its interpretation. A development pipeline based on the CRISP-DM methodology was implemented and the project focused on physiological datasets which were first evaluated independently, then merged and lastly augmented by SMOTE. A range of of models was tested across the different scenarios which in turn allowed for a comprehensive comparison of their effectiveness in affect recognition. The evaluation showed that one of the SMOTE-augmented datasets classified with a RandomForest model achieved he best performance and that ensemble methods generally improved generalization and overall robustness specially when applied to the more feature rich datasets. The results highlight the importance of fine-tuning modeling strategies to each modality and also the feasibility of implementing affect-aware systems in immersive digital environments.