iSAM: Personalizing an Artificial Intelligence Model for Emotion with Pleasure-Arousal-Dominance in Immersive Virtual Reality
Aviv Elor, Asiiah Song · 2020
Emotion, a crucial element of mental health, is not often explored in the field of immersive Virtual Reality (iVR). Enabling personalized affective iVR experiences may be incredibly useful for the expansion and evaluation of serious games. To further this direction of research, we present a playable iVR experience in which the user evaluates the emotion of images through an immersive Self-Assessment Manikin (iSAM). This game explores a pilot system for enabling efficient online fine-tuning of a user's Pleasure-Arousal-Dominance (PAD) emotional model using personalized deep-learning. We discuss adapting the International Affective Picture system (IAPs), in which our Artificial Intelligence (AI) model responds with a personalized image after learning from ten user supplied answers during an iVR session. Lastly, we evaluated our iVR experience with an initial pilot study of four users. Our preliminary results suggest that iSAM can successfully learn from user affect to better predict a `happy' personalized image than static base model.