A fear-directed approach to a novel affective virtual reality game system

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Affective computing methods have presented potential in providing engaging and immersive game experiences, as well as in facilitating the effects of purposeful games related to emotions. There are however inconsistent findings when it comes to the assessment of biofeedback data and its interpretation into a model of emotional states. This is partly due to unfocused approaches over multiple gaming systems, a lack of informed comparisons between measurement devices, and a generalized view of emotions, reducing the complexity and usefulness of affective data. The proposed study will therefore focus only on VR systems and fear-related experiences to build a system that precisely and reliably outputs emotional data in the best possible way suited for VR games. Upon succession, this system will be able to advance emotion-related gameplay development, empathetic social agents for better interactions, as well as provide new evidence for effective psychological training interventions using affective data in video games.