We present AffectivelyVR, a personalized real-time emotion recognition system in Virtual Reality (VR) that enables an emotion-adaptive virtual environment. We used off-the-shelf Electroencephalogram (EEG) and Galvanic Skin Response (GSR) physiological sensors to train user-specific machine learning models while exposing users to affective 360° VR videos. Since emotions are largely dependent on interpersonal experiences and expressed in different ways for different people, we personalize the model instead of generalizing it. By doing this, we achieved an emotion recognition rate of 96.5% using the personalized KNN algorithm, and 83.7% using the generalized SVM algorithm.
Gupta, Kunal, Jovana Lazarevic, Yun Suen Pai, and Mark Billinghurst. "AffectivelyVR: Towards VR Personalized Emotion Recognition." In
26th ACM Symposium on Virtual Reality Software and Technology, pp. 1-3. 2020.