{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T07:25:05Z","timestamp":1764573905989,"version":"3.46.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019629","name":"Universidad de Burgos","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019629","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Virtual Reality"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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 (\n                    <jats:italic>e<\/jats:italic>\n                    .\n                    <jats:italic>g<\/jats:italic>\n                    ., 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 50\u00a0M 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.\n                  <\/jats:p>","DOI":"10.1007\/s10055-025-01232-y","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T05:33:09Z","timestamp":1760419989000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying users of immersive virtual-reality serious games through machine-learning techniques"],"prefix":"10.1007","volume":"29","author":[{"given":"Ines","family":"Miguel-Alonso","sequence":"first","affiliation":[]},{"given":"Juan J.","family":"Rodr\u00edguez","sequence":"additional","affiliation":[]},{"given":"Ana","family":"Serrano-Mamolar","sequence":"additional","affiliation":[]},{"given":"Andres","family":"Bustillo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"1232_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1143947","author":"SG Ali","year":"2023","unstructured":"Ali SG, Wang X, Li P, Jung Y, Bi L, Kim J, Chen Y, Feng DD, Magnenat Thalmann N, Wang J, Sheng B (2023) A systematic review: Virtual-reality-based techniques for human exercises and health improvement. Front Public Health. https:\/\/doi.org\/10.3389\/fpubh.2023.1143947","journal-title":"Front Public Health"},{"key":"1232_CR2","doi-asserted-by":"publisher","unstructured":"Asish SM, Hossain E, Borst CW (2021) Deep learning on eye gaze data to classify student distraction level in an educational VR environment \u2013 honorable mention for best paper award. In: Orlosky J, Reiners D, Weyers B (eds) ICAT-EGVE 2021\u2014international conference on artificial reality and telexistence and eurographics symposium on virtual environments. The Eurographics Association. https:\/\/doi.org\/10.2312\/egve.20211326","DOI":"10.2312\/egve.20211326"},{"key":"1232_CR3","doi-asserted-by":"publisher","unstructured":"Asish SM, Karki BB, Kolahchi N, Sutradhar S (2025) Synthesizing six years of AR\/VR research: a systematic review of machine and deep learning applications. In: 2025 IEEE conference virtual reality and 3D user interfaces (VR). pp 175\u2013185. https:\/\/doi.org\/10.1109\/VR59515.2025.00042","DOI":"10.1109\/VR59515.2025.00042"},{"issue":"1","key":"1232_CR4","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3390\/virtualworlds1010004","volume":"1","author":"SM Asish","year":"2022","unstructured":"Asish SM, Kulshreshth AK, Borst CW (2022) User identification utilizing minimal eye-gaze features in virtual reality applications. Virtual Worlds 1(1):42\u201361. https:\/\/doi.org\/10.3390\/virtualworlds1010004","journal-title":"Virtual Worlds"},{"key":"1232_CR5","unstructured":"Bozkir E, \u00d6zdel S, Wang M, David-John B, Gao H, Butler K, Jain E, Kasneci E (2023) Eye-tracked virtual reality: a comprehensive survey on methods and privacy challenges. https:\/\/arxiv.org\/abs\/2305.14080"},{"issue":"1","key":"1232_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"issue":"2","key":"1232_CR7","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1109\/TG.2021.3064749","volume":"14","author":"P Caserman","year":"2022","unstructured":"Caserman P, Liu S, Gobel S (2022) Full-body motion recognition in immersive- virtual-reality-based exergame. IEEE Trans Games 14(2):243\u2013252. https:\/\/doi.org\/10.1109\/TG.2021.3064749","journal-title":"IEEE Trans Games"},{"issue":"9\u201310","key":"1232_CR8","doi-asserted-by":"publisher","first-page":"5501","DOI":"10.1007\/s11042-019-08348-9","volume":"79","author":"D Checa","year":"2020","unstructured":"Checa D, Bustillo A (2020) A review of immersive virtual reality serious games to enhance learning and training. Multimedia Tools Appl 79(9\u201310):5501\u20135527. https:\/\/doi.org\/10.1007\/s11042-019-08348-9","journal-title":"Multimedia Tools Appl"},{"key":"1232_CR9","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/978-3-030-58468-9_17","volume-title":"Augmented reality, virtual reality, and computer graphics","author":"D Checa","year":"2020","unstructured":"Checa D, Gatto C, Cisternino D, de Paolis LT, Bustillo A (2020) A framework for educational and training immersive virtual reality experiences. In: de Paolis LT, Bourdot P (eds) Augmented reality, virtual reality, and computer graphics. Springer International Publishing, pp 220\u2013228"},{"key":"1232_CR10","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neucom.2018.03.067","volume":"307","author":"M Christ","year":"2018","unstructured":"Christ M, Braun N, Neuffer J, Kempa-Liehr AW (2018) Time series feature extraction on basis of scalable hypothesis tests (tsfresh \u2013 a Python package). Neurocomputing 307:72\u201377. https:\/\/doi.org\/10.1016\/j.neucom.2018.03.067","journal-title":"Neurocomputing"},{"key":"1232_CR11","unstructured":"Christ M, Kempa-Liehr AW and Feindt M (2016) Distributed and parallel time series feature extraction for industrial big data applications."},{"issue":"3","key":"1232_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297. https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach Learn"},{"issue":"1","key":"1232_CR13","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"T Fu","year":"2011","unstructured":"Fu T (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164\u2013181. https:\/\/doi.org\/10.1016\/j.engappai.2010.09.007","journal-title":"Eng Appl Artif Intell"},{"issue":"1117\/12","key":"1232_CR14","first-page":"2542699","volume":"10","author":"AL Gardony","year":"2020","unstructured":"Gardony AL, Lindeman RW, Bruny\u00e9 TT (2020) Eye-tracking for human-centered mixed reality: promises and challenges. Proc SPIE, DOI 10(1117\/12):2542699","journal-title":"Proc SPIE, DOI"},{"issue":"2","key":"1232_CR15","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s10055-024-00970-9","volume":"28","author":"H Guillen-Sanz","year":"2024","unstructured":"Guillen-Sanz H, Checa D, Miguel-Alonso I, Bustillo A (2024) A systematic review of wearable biosensor usage in immersive virtual reality experiences. Virtual Reality 28(2):74. https:\/\/doi.org\/10.1007\/s10055-024-00970-9","journal-title":"Virtual Reality"},{"key":"1232_CR16","doi-asserted-by":"publisher","unstructured":"Komogortsev OV, Karpov A, Price LR and Aragon C (2012) Biometric authentication via oculomotor plant characteristics. In 2012 5th IAPR international conference on biometrics (ICB). pp 413\u2013420. https:\/\/doi.org\/10.1109\/ICB.2012.6199786","DOI":"10.1109\/ICB.2012.6199786"},{"issue":"6","key":"1232_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac4593","volume":"18","author":"P Lapborisuth","year":"2021","unstructured":"Lapborisuth P, Koorathota S, Wang Q, Sajda P (2021) Integrating neural and ocular attention reorienting signals in virtual reality. J Neural Eng 18(6):066052. https:\/\/doi.org\/10.1088\/1741-2552\/ac4593","journal-title":"J Neural Eng"},{"key":"1232_CR18","doi-asserted-by":"publisher","unstructured":"Lebeck K, Ruth K, Kohno T, and Roesner F (2018) towards security and privacy for multi-user augmented reality: foundations with end users. In: 2018 IEEE symposium on security and privacy (SP). pp 392\u2013408. https:\/\/doi.org\/10.1109\/SP.2018.00051","DOI":"10.1109\/SP.2018.00051"},{"key":"1232_CR19","doi-asserted-by":"publisher","unstructured":"Liebers J, Abdelaziz M, Mecke L, Saad A, Auda J, Gruenefeld U, Alt F, and Schneegass S (2021) Understanding user identification in virtual reality through behavioral biometrics and the effect of body normalization. In: Proceedings of the 2021 CHI conference on human factors in computing systems. pp 1\u201311. https:\/\/doi.org\/10.1145\/3411764.3445528","DOI":"10.1145\/3411764.3445528"},{"issue":"2","key":"1232_CR20","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1080\/10447318.2022.2120845","volume":"40","author":"J Liebers","year":"2024","unstructured":"Liebers J, Brockel S, Gruenefeld U, Schneegass S (2024) Identifying users by their hand tracking data in augmented and virtual reality. Int J Hum-Comput Interact 40(2):409\u2013424. https:\/\/doi.org\/10.1080\/10447318.2022.2120845","journal-title":"Int J Hum-Comput Interact"},{"key":"1232_CR21","unstructured":"McKinney W (2011) Pandas: a foundational python library for data analysis and statistics"},{"issue":"1","key":"1232_CR22","doi-asserted-by":"publisher","first-page":"17404","DOI":"10.1038\/s41598-020-74486-y","volume":"10","author":"MR Miller","year":"2020","unstructured":"Miller MR, Herrera F, Jun H, Landay JA, Bailenson JN (2020) Personal identifiability of user tracking data during observation of 360-degree VR video. Sci Rep 10(1):17404. https:\/\/doi.org\/10.1038\/s41598-020-74486-y","journal-title":"Sci Rep"},{"key":"1232_CR23","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3859036","author":"J Jones","year":"2021","unstructured":"Jones J, Duezguen R, Mayer P, Volkamer M, Das S (2021) A literature review on virtual reality authentication. SSRN Electron J. https:\/\/doi.org\/10.2139\/ssrn.3859036","journal-title":"SSRN Electron J"},{"key":"1232_CR24","doi-asserted-by":"publisher","unstructured":"Mustafa T, Matovu R, Serwadda A, and Muirhead N (2018) unsure how to authenticate on your VR headset? In: Proceedings of the fourth ACM international workshop on security and privacy analytics. pp 23\u201330. https:\/\/doi.org\/10.1145\/3180445.3180450","DOI":"10.1145\/3180445.3180450"},{"key":"1232_CR25","unstructured":"Nair V, Guo W, Mattern J, Wang R, O\u2019Brien JF, Rosenberg L, and Song D (2023) Unique identification of 50,000+ virtual reality users from head & hand motion data. In: USENIX security symposium. pp 895\u2013910."},{"issue":"10","key":"1232_CR26","doi-asserted-by":"publisher","first-page":"2944","DOI":"10.3390\/s20102944","volume":"20","author":"I Olade","year":"2020","unstructured":"Olade I, Fleming C, Liang H-N (2020) BioMove: biometric user identification from human kinesiological movements for virtual reality systems. Sensors 20(10):2944. https:\/\/doi.org\/10.3390\/s20102944","journal-title":"Sensors"},{"issue":"85","key":"1232_CR27","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay \u00c9 (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12(85):2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"1232_CR28","doi-asserted-by":"publisher","unstructured":"Pfeuffer K, Geiger MJ, Prange S, Mecke L, Buschek D, and Alt F (2019) Behavioural biometrics in VR: identifying people from body motion and relations in virtual reality. In: Proceedings of the 2019 CHI conference on human factors in computing systems. pp 1\u201312. https:\/\/doi.org\/10.1145\/3290605.3300340","DOI":"10.1145\/3290605.3300340"},{"issue":"2","key":"1232_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2842614","volume":"13","author":"I Rigas","year":"2016","unstructured":"Rigas I, Komogortsev O, Shadmehr R (2016) Biometric recognition via eye movements: saccadic vigor and acceleration cues. ACM Trans Appl Percept 13(2):1\u201321. https:\/\/doi.org\/10.1145\/2842614","journal-title":"ACM Trans Appl Percept"},{"issue":"72","key":"1232_CR30","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3916\/C72-2022-07","volume":"30","author":"E Rodero","year":"2022","unstructured":"Rodero E, Larrea O (2022) Virtual reality with distractors to overcome public speaking anxiety in university students; [Realidad virtual con distractores para superar el miedo a hablar en p\u00fablico en universitarios]. Comunicar 30(72):85\u201396. https:\/\/doi.org\/10.3916\/C72-2022-07","journal-title":"Comunicar"},{"issue":"6","key":"1232_CR31","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386\u2013408","journal-title":"Psychol Rev"},{"key":"1232_CR32","doi-asserted-by":"publisher","first-page":"16861","DOI":"10.1007\/s11042-018-7043-9","volume":"78","author":"H Salehifar","year":"2019","unstructured":"Salehifar H, Bayat P, Majd MA (2019) Eye gesture blink password: a new authentication system with high memorable and maximum password length. Multimedia Tools Appl 78:16861\u201316885","journal-title":"Multimedia Tools Appl"},{"key":"1232_CR33","doi-asserted-by":"publisher","unstructured":"Sch\u00e4fer A, Reis G, and Stricker D (2022) Learning effect of\u00a0lay people in\u00a0gesture-based locomotion in\u00a0virtual reality. pp 369\u2013378. https:\/\/doi.org\/10.1007\/978-3-031-05939-1_25","DOI":"10.1007\/978-3-031-05939-1_25"},{"issue":"76","key":"1232_CR34","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3916\/C76-2023-01","volume":"31","author":"A Serrano-Mamolar","year":"2023","unstructured":"Serrano-Mamolar A, Miguel-Alonso I, Checa D, Pardo-Aguilar C (2023) Towards learner performance evaluation in iVR learning environments using eye-tracking and machine-learning. Comunicar 31(76):9\u201320. https:\/\/doi.org\/10.3916\/C76-2023-01","journal-title":"Comunicar"},{"issue":"118","key":"1232_CR35","first-page":"1","volume":"21","author":"R Tavenard","year":"2020","unstructured":"Tavenard R, Faouzi J, Vandewiele G, Divo F, Androz G, Holtz C, Payne M, Yurchak R, Ru\u00dfwurm M, Kolar K, Woods E (2020) Machine learning toolkit for time series data. J Mach Learn Res 21(118):1\u20136","journal-title":"J Mach Learn Res"},{"key":"1232_CR36","doi-asserted-by":"publisher","first-page":"9859","DOI":"10.1109\/ACCESS.2023.3240071","volume":"11","author":"PP Tricomi","year":"2023","unstructured":"Tricomi PP, Nenna F, Pajola L, Conti M, Gamberini L (2023) You can\u2019t hide behind your headset: user profiling in augmented and virtual reality. IEEE Access 11:9859\u20139875. https:\/\/doi.org\/10.1109\/ACCESS.2023.3240071","journal-title":"IEEE Access"}],"container-title":["Virtual Reality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10055-025-01232-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10055-025-01232-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10055-025-01232-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T07:20:42Z","timestamp":1764573642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10055-025-01232-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1232"],"URL":"https:\/\/doi.org\/10.1007\/s10055-025-01232-y","relation":{},"ISSN":["1434-9957"],"issn-type":[{"type":"electronic","value":"1434-9957"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"18 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"All procedures involving human participants were in accordance with the ethical standards of the Institutional Review Board of the U.S. Army Research Laboratory and with the 1964 Helsinki declaration and its later amendments.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All participants provided their informed consent to participate, in accordance with the standards of the relevant ethics committees (Reference Number: UBU 01\/2022).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}],"article-number":"164"}}