AI-Driven Emotion Prediction in Olfactory VR: A Multimodal Biofeedback Approach Using Russell’s Circumplex Model - Silva Junior, Jonas G. Da; Silva, Meryck F. B. Da; Mottin, Melina; Silva, Cleiver B. Da; Carvalho, Rodrigo M. De; Diniz, Pedro K.; Coronado, Angelo; Galvão Filho, Arlindo Rodrigues; Andrade, Carolina H. (Anais do Simpósio de Realidade Virtual e Aumentada (SVR); 2025: Anais do XXVII Simpósio de Realidade Virtual e Aumentada, 2025)
Despite its importance for immersion in virtual reality (VR), multisensory integration, particularly olfaction, remains underexplored. This study addresses this by developing AI computational models that analyze tri-channel physiological biofeedback (eye tracking, ECG, and GSR) to quantify the sense of presence in VR. Our methodology focuses on interpreting emotional and attentional states from these multimodal signals using supervised learning and explainable AI. Validated using the VREED dataset, which includes synchronized physiological and behavioral data from 360° immersive experiences, this work lays the groundwork for future integration into the SOFIA System. SOFIA is an olfactory-enhanced VR platform that synchronizes scent delivery with user gaze and depth. By enabling real-time emotion classification and presence estimation from physiological parameters, this research empowers the development of adaptive, personalized VR systems. This, in turn, opens new frontiers for more immersive, therapeutically effective, and tailored multisensory environments for digital well-being.