Virtual Reality (VR) technology has evolved significantly, but it often falls short in delivering personalized and adaptable experiences.The "Neuroadaptive VR Enhancement" methodology addressed this challenge by integrating neuroscience and machine learning to dynamically tailor VR content to users' cognitive and emotional states in real-time.Performance metrics, including User Satisfaction Score (USS), Engagement Rate (ER), Classification Accuracy (CA), Adaptation Quality (AQ), Heart Rate Variability (HRV) data, Task Performance metrics, Content Relevance Score (CRS), Presence (PRE), Immersion (IMM), System Usability Scale (SUS), and Usability Score (US), were employed to gauge effectiveness.Results indicate a substantial increase in user satisfaction and engagement.High CA demonstrates accurate interpretation of user states.User feedback via AQ scores underscores alignment between adaptations and user preferences.HRV data reveals insights into emotional states.Task performance metrics show efficiency and effectiveness.Users consistently report higher CRS ratings, confirming content relevance.The research contributes to the advancement of VR by addressing personalization and adaptation challenges, offering potential applications in gaming, education, healthcare, and therapy.This study pioneers user-centric VR experiences, envisioning a more personalized, emotionally resonant, and engaging VR future.
Authors: Ahmad Heryanto, Yonis Gulzar, Gene Marck
DOI: https://doi.org/10.22362/ijcert/2023/v10/i02/v10i0203
Publish Year: 2023
