Authors: Nandhini P,, Ms. N. Sukanya
Abstract: Virtual Reality (VR) technologies are increasingly integrated into diverse domains, necessitating a deeper understanding of user experience and emotional engagement. This study explores an AI-based emotion detection framework that leverages biofeedback signals—such as heart rate variability, skin conductance, and facial expression data—within immersive VR environments. Machine learning algorithms are employed to analyze these multimodal inputs in real-time, enabling the detection and classification of user emotions. Preliminary results suggest improved immersion and user satisfaction, highlighting the potential of biofeedback-driven AI in creating emotionally intelligent VR . This study explores an AI-based emotion detection framework that leverages biofeedback signals—such as heart rate variability (HRV), skin conductance response (SCR), and facial expression data—within immersive VR environments. The proposed framework integrates sensor fusion techniques to combine diverse signal modalities, addressing challenges related to data synchronization, noise reduction, and individual variability. Experimental evaluations were conducted in controlled VR scenarios, assessing emotion recognition performance and its impact on user immersion and satisfaction.Preliminary results demonstrate the effectiveness of in emotion-aware applications such as therapeutic interventions, adaptive learning systems, and emotionally intelligent game design. This work contributes to the growing field of affective computing in VR by presenting a robust, real-time emotion detection model grounded in biofeedback and artificial intelligence.
DOI: http://doi.org/
