Physiological-Based Difficulty Assessment for Virtual Reality Rehabilitation Games

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This paper proposes an empirical framework that aims to classify difficulty according to the player's physiological response. As part of the experimental protocol, a simple puzzle-based Virtual Reality (VR) videogame with three levels of difficulty was developed, each targeting a distinct region of the valence-arousal space. A study involving 32 participants was conducted, during which physiological responses (EDA, ECG, Respiration), were measured alongside emotional ratings, which were self-assessed using the Self-Assessment Manikin (SAM) during gameplay. Statistical analysis of the self-reports verified the effectiveness of the three levels in eliciting different emotions. Furthermore, classification using a Support Vector Machine (SVM) was performed to predict difficulty considering the physiological responses associated with each level. Results report an overall F1-score of 74.05% in detecting the three levels of difficulty, which validates the adopted methodology and encourages further research with a larger dataset.

Original languageEnglish
Title of host publicationFoundations of Digital Games 2023 (FDG 2023), April 12–14, 2023, Lisbon, Portugal
EditorsPhil Lopes, Filipe Luz, Antonios Liapis, Henrik Engstrom
Place of PublicationNew York
PublisherACM - Association for Computing Machinery
Number of pages4
ISBN (Electronic)978-145039856-5
Publication statusPublished - Apr 2023
Event18th International Conference on the Foundations of Digital Games, FDG 2023 - Lisbon, Portugal
Duration: 11 Apr 202314 Apr 2023

Publication series

NameACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery


Conference18th International Conference on the Foundations of Digital Games, FDG 2023


  • Affective computing
  • emotion assessment
  • games
  • multimodal dataset
  • virtual reality


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