Rodrigues, PedroFonseca, MicaelaLopes, Phil2023-07-142023-07-142023-04978-145039856-5PURE: 66001122PURE UUID: a2219ab0-d689-4535-9579-3b1d95e89d9fScopus: 85153562758ORCID: /0000-0001-7946-4825/work/151427190http://hdl.handle.net/10362/155271Funding Information: This work is supported by Fundação para a Ciência e Tecnologia (FCT), under HEI-Lab R&D Unit (UIDB/05380/2020) and Project PlayersAll: media agency and empowerment (EXPL/COM-OUT/088 2/2021). Publisher Copyright: © 2023 Owner/Author.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.4562212engAffective computingemotion assessmentgamesmultimodal datasetvirtual realityHuman-Computer InteractionComputer Networks and CommunicationsComputer Vision and Pattern RecognitionSoftwareconference object10.1145/3582437.3587187https://www.scopus.com/pages/publications/85153562758