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

TL;DR

This paper introduces a novel approach for dynamic difficulty adjustment in VR exergames by using experience-driven procedural content generation, creating personalized levels that adapt to players' capabilities to enhance motivation and long-term engagement.

Contribution

It proposes a new method combining neural network-based maze generation and deep reinforcement learning for adaptive difficulty in VR exercise games, moving beyond simple parameter tuning.

Findings

Prototype successfully generates personalized mazes with exercise rooms.

User study indicates increased motivation and engagement.

Adaptive levels match player capabilities effectively.

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.