Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

• URL: http://arxiv.org/abs/2108.08762v1
• Date: Thu, 19 Aug 2021 16:06:16 GMT
• Title: Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation
• Authors: Tobias Huber, Silvan Mertes, Stanislava Rangelova, Simon Flutura, Elisabeth Andr\'e
• Abstract summary: We propose to use experience-driven Procedural Content Generation for DDA in VR exercise games. We implement an initial prototype in which the player must traverse a maze that includes several exercise rooms. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze.
• Score: 0.4899818550820576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the player's current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame. As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.

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