Automated Personalized Exposure Therapy Based on Physiological Measures Using Experience-Driven Procedural Content Generation

Authors

  • Athar Mahmoudi-Nejad University of Alberta

Keywords:

Procedural Content Generation, Experience-driven Procedural Content Generation, Reinforcement Learning, Virtual Reality Exposure Therapy, User Experience Model

Abstract

Our research focuses on personalized virtual reality exposure therapy (VRET) based on the Experience-Driven Procedural Content Generation (EDPCG) framework. There are existing approaches for personalized VRET; however, they are subjective and require hand-authored and predefined rules that may not generalize to all subjects. We propose a framework to personalize VRET based on predicting subjects' experiences via physiological sensors and machine learning algorithms. The framework then automatically adapts exposure parameters based on the subject's physiological response using a PCG method. We intend to conduct two human subject studies for arachnophobia and fear of public speaking.

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Published

2021-10-04

How to Cite

Mahmoudi-Nejad, A. (2021). Automated Personalized Exposure Therapy Based on Physiological Measures Using Experience-Driven Procedural Content Generation. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 17(1), 232-235. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/18914