Acta Informatica Pragensia | 2026
Authors: Balogh Z.; Fodor K.; Magdin M.; Reichel J.; Kopják J.; Koprda Š.; Polák M.
DOI: 10.18267/J.AIP.305
Journal: Acta Informatica Pragensia
Year: 2026
Publisher: Prague University of Economics and Business
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
Background: Virtual reality (VR) integrated with internet of things (IoT) wearable devices offers innovative approaches to mental health interventions by enabling real-time physiological monitoring during immersive therapeutic experiences. Objective: This study aims to evaluate the effectiveness of VR therapeutic games in identifying and measuring emotional responses through physiological signals (heart rate and galvanic skin response) and to classify these responses using machine learning. Methods: We conduct experiments with 103 participants (aged 6–57 years) using FearTherapy, a custom VR game featuring interactions with four animals (Hermit, Bee, Wolf, Spider). Physiological data are collected using Samsung Galaxy Watch 5 for heart rate and Arduino Uno with a galvanic skin response (GSR) sensor. After preprocessing, 55 valid sessions remain for analysis. Individual baseline heart rate values are established and random forest classification with grid search optimization and 10-fold cross-validation is performed. Results: GSR emerges as the most influential feature for classifying emotional states, followed by heart rate difference and baseline reference values. Highest emotional arousal occurs during Spider and Bee interactions. The random forest model achieves 69% cross-validation accuracy and 81% test set accuracy. The model performs well overall but encounters challenges distinguishing Bee from Hermit and Wolf from Spider, suggesting overlapping emotional states. Conclusion: VR therapeutic games combined with IoT physiological monitoring can effectively measure and classify emotional responses. The findings support development of personalized, emotionally adaptive therapeutic interventions for anxiety and phobia treatment, emphasizing the importance of individualized baseline measurements for accurate emotional state assessment. Copyright: © 2026 by the author(s). Licensee Prague University of Economics and Business, Czech Republic. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (CC BY 4.0).
Fear measurement; Galvanic skin response; Heart rate; Internet of things; Machine learning; Physiological monitoring; Sensory network; Therapeutic game; Virtual reality