Identifying users of immersive virtual-reality serious games through machine-learning techniques

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...

Descripción completa

Detalles Bibliográficos
Autores: Miguel Alonso, Inés, Rodríguez Diez, Juan José, Serrano Mamolar, Ana, Bustillo Iglesias, Andrés
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/10986
Acceso en línea:https://hdl.handle.net/10259/10986
Access Level:acceso abierto
Palabra clave:Virtual Reality
Random Forest
Head Mounted Display
User identification
Machine Learning
Open-Access Datasets
Realidad virtual
Virtual reality
id ES_45388bb67541bc1af73ef802aaec3a25
oai_identifier_str oai:riubu.ubu.es:10259/10986
network_acronym_str ES
network_name_str España
repository_id_str
spelling Identifying users of immersive virtual-reality serious games through machine-learning techniquesMiguel Alonso, InésRodríguez Diez, Juan JoséSerrano Mamolar, AnaBustillo Iglesias, AndrésVirtual RealityRandom ForestHead Mounted DisplayUser identificationMachine LearningOpen-Access DatasetsRealidad virtualVirtual realityUser 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.This 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) and through “ERDF A way of making Europe” or European Union NextGenerationEU/PRTR funding; the HumanAid Project (Reference Number: TED2021-129485B-C43) funded through the Spanish Ministry of Science and Innovation and the Ministry of Science, Innovation and Universities (FPU21/01978).Springer Nature202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10259/10986reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésVirtual Reality. 2025. V. 29. n. 164https://doi.org/10.1007/s10055-025-01232-yAtribución 4.0 Internacionalinfo:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/109862026-05-28T07:56:11Z
dc.title.none.fl_str_mv Identifying users of immersive virtual-reality serious games through machine-learning techniques
title Identifying users of immersive virtual-reality serious games through machine-learning techniques
spellingShingle Identifying users of immersive virtual-reality serious games through machine-learning techniques
Miguel Alonso, Inés
Virtual Reality
Random Forest
Head Mounted Display
User identification
Machine Learning
Open-Access Datasets
Realidad virtual
Virtual reality
title_short Identifying users of immersive virtual-reality serious games through machine-learning techniques
title_full Identifying users of immersive virtual-reality serious games through machine-learning techniques
title_fullStr Identifying users of immersive virtual-reality serious games through machine-learning techniques
title_full_unstemmed Identifying users of immersive virtual-reality serious games through machine-learning techniques
title_sort Identifying users of immersive virtual-reality serious games through machine-learning techniques
dc.creator.none.fl_str_mv Miguel Alonso, Inés
Rodríguez Diez, Juan José
Serrano Mamolar, Ana
Bustillo Iglesias, Andrés
author Miguel Alonso, Inés
author_facet Miguel Alonso, Inés
Rodríguez Diez, Juan José
Serrano Mamolar, Ana
Bustillo Iglesias, Andrés
author_role author
author2 Rodríguez Diez, Juan José
Serrano Mamolar, Ana
Bustillo Iglesias, Andrés
author2_role author
author
author
dc.subject.none.fl_str_mv Virtual Reality
Random Forest
Head Mounted Display
User identification
Machine Learning
Open-Access Datasets
Realidad virtual
Virtual reality
topic Virtual Reality
Random Forest
Head Mounted Display
User identification
Machine Learning
Open-Access Datasets
Realidad virtual
Virtual reality
description 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10259/10986
url https://hdl.handle.net/10259/10986
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Virtual Reality. 2025. V. 29. n. 164
https://doi.org/10.1007/s10055-025-01232-y
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407146691526656
score 15.812455

Similares en LA Referencia