Procedural content generation based on a genetic algorithm in a serious game for obstructive sleep apnea

In this paper, a procedural content generation (PCG) technique that was incorporated in a novel serious game for obstructive sleep apnea (OSA) is presented. The technique is based on a genetic algorithm and aims to enhance user engagement and deliver educational material tailored to user needs. The genetic algorithm monitors user choices and game progress by means of two fitness functions that dictate suitable candidates to produce offspring in each generation. An initial validation in terms of user experience was conducted, by deploying three different versions of the serious game. Version A and B incorporated the genetic algorithm, along with mechanisms for automated game difficulty adjustment. Version A was designed to be difficult and frustrating while version B to adjust difficulty smoothly. Version C did not display any adaptive properties. 42 participants were recruited and split in two groups to play two versions of the game, A and C or B and C, without any prior knowledge of differences between them. After each session, two modules of the Game Experience Questionnaire (GEQ) were applied. The obtained results reveal statistically significant differences regarding user perception in terms of competence, challenge and negative experience for versions A and C respectively, and competence and negative experience for versions B and C respectively. Version B achieved better GEQ scores than version C while version A resulted in worse GEQ scores than version C.