RT info:eu-repo/semantics/article
T1 Identifying users of immersive virtual-reality serious games through machine-learning techniques
A1 Miguel Alonso, Inés
A1 Rodríguez Diez, Juan José
A1 Serrano Mamolar, Ana
A1 Bustillo Iglesias, Andrés
K1 Virtual Reality
K1 Random Forest
K1 Head Mounted Display
K1 User identification
K1 Machine Learning
K1 Open-Access Datasets
K1 Realidad virtual
K1 Virtual reality
AB User identification is currently an open issue in immersive Virtual Reality (iVR) environments. Three main goals are usually associated with the use of tracking-data and Machine-Learning (ML) techniques: safeguarding privacy, user authentication, and user-experience customization. However, research to date has only involved very limited recordings of user data (e.g., on a single session and for low-interactive situations), rare in real iVR environments. So, the research gap between real iVR data and ML techniques for user identification is addressed in this paper. To do so, a 3-session iVR experience of operating a bridge crane is considered. In this simple yet highly interactive learning action, the dataset records of user performance show rapid changes between one experience and another. Eye, head, and hand movements of 64 users of similar age and with comparable previous experience were all recorded while engaged with the experience. The final raw dataset had a size of approximately 50M data points with 25 attributes that were mainly temporal series values. Secondly, different ML algorithms were used for user identification: Decision Tree, Random Forest, XGBoost, k-Nearest Neighbors, Support Vector Machines, and Multilayer Perceptron. The results showed that ML ensemble learning techniques, particularly Random Forest, were the most suitable solutions on the basis of different measures for the prediction of user identity. Additionally, the inclusion of stress and no-stress conditions significantly enhanced model performance, highlighting the importance of data diversity. Temporal segmentation revealed that user identification during later phases of the exercise was slightly more effective, due to increased individual variability. Finally, a minimum duration of the iVR experience was identified as a requirement to assure high identification rates.
PB Springer
SN 1359-4338
YR 2025
FD 2025-09
LK https://hdl.handle.net/10259/10992
UL https://hdl.handle.net/10259/10992
LA eng
NO his study was partially funded through the ACIS project (Reference Number: INVESTUN/21/BU/0002) of the Consejería de Empleo e Industria of the Junta de Castilla y León (Spain); the REMAR Project (Reference Number: CPP2022-009724) supported by the Ministry of Science and Innovation of Spain (MCIN/AEI/10.13039/501100011033) andthrough “ERDF A way of making Europe” or European Union NextGenerationEU/PRTR funding; the HumanAidProject (Reference Number: TED2021-129485B-C43) funded through the Spanish Ministry of Science and Innovationand the Ministry of Science, Innovation and Universities (FPU21/01978).
DS Repositorio Institucional de la Universidad de Burgos
RD 19-abr-2026