Using Heart Rate and Machine Learning for VR Horror Game Personalization

In this paper, we explore if we can personalize horror games in Virtual Reality using auditory and visual stimuli with the help of different machine learning algorithms. Based on the heart rate of the subjects we personalize the sound and lighting effects of the game in two different environmental settings. Gradient Boosted Tree Regression, Random Forest Regression, and Tree Ensemble Regression were used to predict which sound and lighting effects should be used in subsequent levels to increase the horror aspect. In order to have a realistic game experience and due to the ongoing coronavirus pandemic, participants were recruited online. Participants could play the game wherever and whenever they wanted. The participants were also asked to complete Self-Assessment Manikin tests after playing the game. We present our discussions and observations of how different factors affect the heart rate in the game and if the heart rate data aligns with the participant’s Self-Assessment Manikin test data.