Adaptive Virtual Reality Solutions: A Literature Review
Resumo
Virtual Reality technology emerges as a versatile tool with applications ranging from gaming to therapy. Particularly, Adaptive Virtual Reality (AVR) stands out as a promising field, employing adaptive mechanisms to automatically customize the user experience according to individual preferences and needs. The use cases for this technology are broad, and so is the technical landscape. AVR technology enables, for instance, educational platforms that adapt to the user’s learning style, upper limb rehabilitation with auto-adjustable difficulty, and horror games that detect and incorporate the user’s most feared elements. Implementation-wise, some studies utilize Neural Networks and Support Vector Machines to adapt the system’s logic, while others use Fuzzy Logic Systems, for example. Additionally, some works integrate physiological sensors to estimate user stress, cognitive workload, or cybersickness. This technological and practical diversity poses a challenge for researchers and developers seeking to build AVR-based applications. To assist these professionals, this work reviews 30 studies that propose, develop, and assess AVR-based solutions. It provides a technological overview of these systems and classifies them into four domain groups: healthcare, learning/training, entertainment, and cybersickness mitigation. This work also presents key considerations and guidelines for designing and assessing AVR-based systems.
Palavras-chave:
Review, Adaptive Virtual Reality, Learning, Training, Healthcare, Entertainment, Cybersickness Mitigation
Referências
Aguilar Reyes, C. I., Wozniak, D., Ham, A., & Zahabi, M. (2023). Design and evaluation of an adaptive virtual reality training system. Virtual Reality, 27(3), 2509–2528. DOI: 10.1007/s10055-023-00827-7
Ballester, B. R., Maier, M., Domingo, D. A., Aguilar, A., Mura, A., Pareja, L. T., Esteve, M. F. G., & Verschure, P. F. M. J. (2019). Adaptive VR-based rehabilitation to prevent deterioration in adults with cerebral palsy. International Conference on Virtual Rehabilitation, ICVR 2019-July. DOI: 10.1109/ICVR46560.2019.8994754
Bekele, E., Wade, J. W., Bian, D., Zhang, L., Zheng, Z., Swanson, A., Sarkar, M., Warren, Z., & Sarkar, N. (2014). Multimodal interfaces and sensory fusion in VR for social interactions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8525(1), 14–24. DOI: 10.1007/978-3-319-07458-0_2
Bekele, E., Young, M., Zheng, Z., Zhang, L., Swanson, A., Johnston, R., Davidson, J., Warren, Z., & Sarkar, N. (2013). A step towards adaptive multimodal virtual social interaction platform for children with autism. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8010(2), 464–473. DOI: 10.1007/978-3-642-39191-0_51
Bergsnev, K., & Sánchez Laws, A. L. (2022). Personalizing virtual reality for the research and treatment of fear-related disorders: A mini review. Frontiers in Virtual Reality, 3, 834004. DOI: 10.3389/frvir.2022.834004
Bosse, T., Gerritsen, C., de Man, J., & Tolmeijer, S. (2015). Adaptive training for aggression de-escalation. In Artificial Life and Intelligent Agents: First International Symposium, ALIA 2014, Revised Selected Papers 1, 80–93. DOI: 10.1007/978-3-319-18084-7_7
Bouatrous, A., Meziane, A., Zenati, N., & Hamitouche, C. (2023). A new adaptive VR-based exergame for hand rehabilitation after stroke. Multimedia Systems, 29(6), 3385–3402. DOI: 10.1007/s00530-023-01180-0
Brooke, J. et al. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189, 194, 4–7.
Chiossi, F., Turgut, Y., Welsch, R., & Mayer, S. (2022). Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience. Big Data and Cognitive Computing, 6(2). DOI: 10.3390/bdcc6020055
de Lima, E. S., Silva, B. M. C., & Galam, G. T. (2022). Adaptive virtual reality horror games based on machine learning and player modeling. Entertainment Computing, 43. DOI: 10.1016/j.entcom.2022.100515
Finseth, T., Dorneich, M. C., Keren, N., Franke, W. D., & Vardeman, S. (2024). Virtual reality adaptive training for personalized stress inoculation. Human Factors. DOI: 10.1177/00187208241241968
Fricoteaux, L., Thouvenin, I., & Mestre, D. (2014). Gulliver: a decision-making system based on user observation for an adaptive training in informed virtual environments. Engineering Applications of Artificial Intelligence, 33, 47–57. DOI: 10.1016/j.engappai.2014.03.005
Le Groux, S., Manzolli, J., & Verschure, P. F. M. J. (2007). Vr-roboser: real-time adaptive sonification of virtual environments based on avatar behavior. In Proceedings of the 7th international conference on New interfaces for musical expression, 371–374. DOI: 10.1145/1279740.1279822
Gu, G., & Frasson, C. (2017). Virtual sophrologist: a virtual reality neurofeedback relaxation training system. In Brain Function Assessment in Learning: First International Conference, BFAL 2017, Proceedings 1, 176–185. DOI: 10.1007/978-3-319-67615-9_16
Gu, Y., Sosnovsky, S., & Ullrich, C. (2015). Safechild: An intelligent virtual reality environment for training pedestrian safety skills. Lecture Notes in Computer Science, 9307, 141–154. DOI: 10.1007/978-3-319-24258-3_11
Harris, D., Donaldson, R., Bray, M., Arthur, T., Wilson, M., & Vine, S. (2024). Attention computing for enhanced visuomotor skill performance: Testing the effectiveness of gaze-adaptive cues in virtual reality golf putting. Multimedia Tools and Applications. DOI: 10.1007/s11042-023-17973-4
Bermudez i Badia, S., Velez Quintero, L., Cameirao, M. S., Chirico, A., Triberti, S., Cipresso, P., & Gaggioli, A. (2019). Toward emotionally adaptive virtual reality for mental health applications. IEEE journal of biomedical and health informatics, 23(5), 1877–1887. DOI: 10.1109/JBHI.2018.2878846
Islam, R., Ang, S., & Quarles, J. (2021). Cybersense: A closed-loop framework to detect cybersickness severity and adaptively apply reduction techniques. In 2021 IEEE Conference on virtual reality and 3d user interfaces abstracts and workshops (VRW), 148–155. DOI: 10.1109/VRW52623.2021.00035
Jeelani, I., Han, K., & Albert, A. (2017). Development of immersive personalized training environment for construction workers. In Computing in civil engineering 2017, 407–415. DOI: 10.1061/9780784480830.050
Kennedy, R. S., Lane, N. E., Berbaum, K. S., & Lilienthal, M. G. (1993). Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The international journal of aviation psychology, 3(3), 203–220.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University 33(2004), 1–26.
Kritikos, J., Alevizopoulos, G., & Koutsouris, D. (2021). Personalized virtual reality human-computer interaction for psychiatric and neurological illnesses: A dynamically adaptive virtual reality environment that changes according to real-time feedback from electrophysiological signal responses. Frontiers in Human Neuroscience, 15. DOI: 10.3389/fnhum.2021.596980
Landsberg, C. R., Van Buskirk, W. L., Astwood Jr., R. S., Mercado, A. D., & Aakre, A. J. (2010). Adaptive training considerations for use in simulation-based systems. Naval Air Warfare Center Training Systems Division: Special Report (2010), 9. DOI: 10.21236/ADA535421
Lang, Y., Wei, L., Xu, F., Zhao, Y., & Yu, L. (2018). Synthesizing personalized training programs for improving driving habits via virtual reality. In 2018 IEEE conference on virtual reality and 3D user interfaces (VR), 297–304. DOI: 10.1109/VR.2018.8448290
Lawson, B. (2014). Motion sickness symptomatology and origins. In Handbook of Virtual Environment: Design, implementation, and applications (2nd ed.), 532–587. DOI: 10.1201/b17360-29
Li, W., Huang, H., Solomon, T., Esmaeili, B., & Yu, L. (2022). Synthesizing personalized construction safety training scenarios for VR training. IEEE Transactions on Visualization and Computer Graphics, 28(5), 1993–2002. DOI: 10.1109/TVCG.2022.3150510
Liang, H., Liu, S., Wang, Y., Pan, J., Fu, J., & Yuan, Y. (2023). EEG-Based VR Scene Adaptive Generation System for Regulating Emotion. In 2023 9th International Conference on Virtual Reality (ICVR), 361–368. DOI: 10.1109/ICVR57957.2023.10169325
Lin, Y., & Wang, S. (2019). The study and application of adaptive learning method based on virtual reality for engineering education. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11903 LNCS, 372–383. DOI: 10.1007/978-3-030-34113-8_31
Livatino, S., & Hochleitner, C. (2008). Simple Guidelines for Testing VR Applications. DOI: 10.5772/5925
Mariani, A., Pellegrini, E., Enayati, N., Kazanzides, P., Vidotto, M., & de Momi, E. (2018). Design and evaluation of a performance-based adaptive curriculum for robotic surgical training: a pilot study. In 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2162–2165. DOI: 10.1109/EMBC.2018.8512728
Mourning, R., & Tang, Y. (2016). Virtual reality social training for adolescents with high-functioning autism. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4848–4853. DOI: 10.1109/SMC.2016.7844996
Mousavi, S. M. H., Khaertdinov, B., Jeuris, P., Hortal, E., Andreoletti, D., & Giordano, S. (2023). Emotion recognition in adaptive virtual reality settings: Challenges and opportunities. In CEUR-WS Proceedings, 3517, 1–20.
Ojha, A., Narain, S., Raj, A., Agrawal, T., Wadhwa, B., & Joshi, M. (2024). Dynamic virtual reality horror sports enhanced by artificial intelligence and player modeling. Multimedia Tools and Applications. DOI: 10.1007/s11042-024-18414-6
Parsons, T. D. (2021). Ethical challenges of using virtual environments in the assessment and treatment of psychopathological disorders. Journal of Clinical Medicine, 10(3), 378. DOI: 10.3390%2Fjcm10030378
Parsons, T. D., McMahan, T., & Asbee, J. (2024). Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store. Virtual Reality, 28(1). DOI: 10.1007/s10055-023-00927-4
PEDro. (2024). PEDro scale. [link]
Porcino, T., Rodrigues, E. O., Bernardini, F., Trevisan, D., & Clua, E. (2022). Identifying cybersickness causes in virtual reality games using symbolic machine learning algorithms. Entertainment Computing, 41, 100473. DOI: 10.1016/j.entcom.2021.100473
Reidy, L., Chan, D., Nduka, C., & Gunes, H. (2020). Facial electromyography-based adaptive virtual reality gaming for cognitive training. ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction, 174–183. DOI: 10.1145/3382507.3418845
Rovira, A., & Slater, M. (2022). Encouraging bystander helping behaviour in a violent incident: a virtual reality study using reinforcement learning. Scientific Reports, 12(1). DOI: 10.1038/s41598-022-07872-3
Scott, E., Soria, A., & Campo, M. (2016). Adaptive 3D virtual learning environments—A review of the literature. IEEE Transactions on Learning Technologies, 10(3), 262–276. DOI: 10.1109/TLT.2016.2609910
Senno, B., & Barcha, P. (2018). Customizing user experience with adaptive virtual reality. In Companion Proceedings of the 23rd International Conference on Intelligent User Interfaces, Article 42. DOI: 10.1145/3180308.3180351
Siu, K.-C., Best, B. J., Kim, J. W., Oleynikov, D., & Ritter, F. E. (2016). Adaptive virtual reality training to optimize military medical skills acquisition and retention. Military Medicine, 181(5), 214–220. DOI: 10.7205/MILMED-D-15-00164
Stranick, T., & Lopez, C. (2022). Adaptive virtual reality exergame: Promoting physical activity among workers. Journal of Computing and Information Science in Engineering, 22(3). DOI: 10.1115/1.4053002
Uyan, U., & Celikcan, U. (2024). CDMS: A real-time system for EEG-guided cybersickness mitigation through adaptive adjustment of VR content factors. Displays, 83, 102704. DOI: 10.1016/j.displa.2024.102704
Wood, K., Quevedo, A. J. U., Penuela, L., Perera, S., & Kapralos, B. (2021). Virtual reality assessment and customization using physiological measures: A literature analysis. In Proceedings of the 23rd Symposium on Virtual and Augmented Reality, 64–73. DOI: 10.1145/3488162.3488228
Wu, F., & Rosenberg, E. S. (2022). Adaptive field-of-view restriction: Limiting optical flow to mitigate cybersickness in virtual reality. In Proceedings of the 28th ACM Symposium on Virtual Reality Software and Technology, 1–11. DOI: 10.1145/3562939.3565611
Yang, X., Wang, D., & Zhang, Y. (2016). An adaptive strategy for an immersive visuo-haptic attention training game. In Haptics: Perception, Devices, Control, and Applications: 10th International Conference, EuroHaptics 2016, Proceedings, Part I 10, 441–451. DOI: 10.1007/978-3-319-42321-0_41
Yu, S.-J., Hsueh, Y.-L., Sun, J. C.-Y., & Liu, H.-Z. (2021). Developing an intelligent virtual reality interactive system based on the ADDIE model for learning pour-over coffee brewing. Computers and Education: Artificial Intelligence, 2. DOI: 10.1016/j.caeai.2021.100030
Zahabi, M., & Abdul Razak, A. M. (2020). Adaptive virtual reality-based training: a systematic literature review and framework. Virtual Reality, 24(4), 725–752. DOI: 10.1007/s10055-020-00434-w
Ballester, B. R., Maier, M., Domingo, D. A., Aguilar, A., Mura, A., Pareja, L. T., Esteve, M. F. G., & Verschure, P. F. M. J. (2019). Adaptive VR-based rehabilitation to prevent deterioration in adults with cerebral palsy. International Conference on Virtual Rehabilitation, ICVR 2019-July. DOI: 10.1109/ICVR46560.2019.8994754
Bekele, E., Wade, J. W., Bian, D., Zhang, L., Zheng, Z., Swanson, A., Sarkar, M., Warren, Z., & Sarkar, N. (2014). Multimodal interfaces and sensory fusion in VR for social interactions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8525(1), 14–24. DOI: 10.1007/978-3-319-07458-0_2
Bekele, E., Young, M., Zheng, Z., Zhang, L., Swanson, A., Johnston, R., Davidson, J., Warren, Z., & Sarkar, N. (2013). A step towards adaptive multimodal virtual social interaction platform for children with autism. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8010(2), 464–473. DOI: 10.1007/978-3-642-39191-0_51
Bergsnev, K., & Sánchez Laws, A. L. (2022). Personalizing virtual reality for the research and treatment of fear-related disorders: A mini review. Frontiers in Virtual Reality, 3, 834004. DOI: 10.3389/frvir.2022.834004
Bosse, T., Gerritsen, C., de Man, J., & Tolmeijer, S. (2015). Adaptive training for aggression de-escalation. In Artificial Life and Intelligent Agents: First International Symposium, ALIA 2014, Revised Selected Papers 1, 80–93. DOI: 10.1007/978-3-319-18084-7_7
Bouatrous, A., Meziane, A., Zenati, N., & Hamitouche, C. (2023). A new adaptive VR-based exergame for hand rehabilitation after stroke. Multimedia Systems, 29(6), 3385–3402. DOI: 10.1007/s00530-023-01180-0
Brooke, J. et al. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189, 194, 4–7.
Chiossi, F., Turgut, Y., Welsch, R., & Mayer, S. (2022). Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience. Big Data and Cognitive Computing, 6(2). DOI: 10.3390/bdcc6020055
de Lima, E. S., Silva, B. M. C., & Galam, G. T. (2022). Adaptive virtual reality horror games based on machine learning and player modeling. Entertainment Computing, 43. DOI: 10.1016/j.entcom.2022.100515
Finseth, T., Dorneich, M. C., Keren, N., Franke, W. D., & Vardeman, S. (2024). Virtual reality adaptive training for personalized stress inoculation. Human Factors. DOI: 10.1177/00187208241241968
Fricoteaux, L., Thouvenin, I., & Mestre, D. (2014). Gulliver: a decision-making system based on user observation for an adaptive training in informed virtual environments. Engineering Applications of Artificial Intelligence, 33, 47–57. DOI: 10.1016/j.engappai.2014.03.005
Le Groux, S., Manzolli, J., & Verschure, P. F. M. J. (2007). Vr-roboser: real-time adaptive sonification of virtual environments based on avatar behavior. In Proceedings of the 7th international conference on New interfaces for musical expression, 371–374. DOI: 10.1145/1279740.1279822
Gu, G., & Frasson, C. (2017). Virtual sophrologist: a virtual reality neurofeedback relaxation training system. In Brain Function Assessment in Learning: First International Conference, BFAL 2017, Proceedings 1, 176–185. DOI: 10.1007/978-3-319-67615-9_16
Gu, Y., Sosnovsky, S., & Ullrich, C. (2015). Safechild: An intelligent virtual reality environment for training pedestrian safety skills. Lecture Notes in Computer Science, 9307, 141–154. DOI: 10.1007/978-3-319-24258-3_11
Harris, D., Donaldson, R., Bray, M., Arthur, T., Wilson, M., & Vine, S. (2024). Attention computing for enhanced visuomotor skill performance: Testing the effectiveness of gaze-adaptive cues in virtual reality golf putting. Multimedia Tools and Applications. DOI: 10.1007/s11042-023-17973-4
Bermudez i Badia, S., Velez Quintero, L., Cameirao, M. S., Chirico, A., Triberti, S., Cipresso, P., & Gaggioli, A. (2019). Toward emotionally adaptive virtual reality for mental health applications. IEEE journal of biomedical and health informatics, 23(5), 1877–1887. DOI: 10.1109/JBHI.2018.2878846
Islam, R., Ang, S., & Quarles, J. (2021). Cybersense: A closed-loop framework to detect cybersickness severity and adaptively apply reduction techniques. In 2021 IEEE Conference on virtual reality and 3d user interfaces abstracts and workshops (VRW), 148–155. DOI: 10.1109/VRW52623.2021.00035
Jeelani, I., Han, K., & Albert, A. (2017). Development of immersive personalized training environment for construction workers. In Computing in civil engineering 2017, 407–415. DOI: 10.1061/9780784480830.050
Kennedy, R. S., Lane, N. E., Berbaum, K. S., & Lilienthal, M. G. (1993). Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The international journal of aviation psychology, 3(3), 203–220.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University 33(2004), 1–26.
Kritikos, J., Alevizopoulos, G., & Koutsouris, D. (2021). Personalized virtual reality human-computer interaction for psychiatric and neurological illnesses: A dynamically adaptive virtual reality environment that changes according to real-time feedback from electrophysiological signal responses. Frontiers in Human Neuroscience, 15. DOI: 10.3389/fnhum.2021.596980
Landsberg, C. R., Van Buskirk, W. L., Astwood Jr., R. S., Mercado, A. D., & Aakre, A. J. (2010). Adaptive training considerations for use in simulation-based systems. Naval Air Warfare Center Training Systems Division: Special Report (2010), 9. DOI: 10.21236/ADA535421
Lang, Y., Wei, L., Xu, F., Zhao, Y., & Yu, L. (2018). Synthesizing personalized training programs for improving driving habits via virtual reality. In 2018 IEEE conference on virtual reality and 3D user interfaces (VR), 297–304. DOI: 10.1109/VR.2018.8448290
Lawson, B. (2014). Motion sickness symptomatology and origins. In Handbook of Virtual Environment: Design, implementation, and applications (2nd ed.), 532–587. DOI: 10.1201/b17360-29
Li, W., Huang, H., Solomon, T., Esmaeili, B., & Yu, L. (2022). Synthesizing personalized construction safety training scenarios for VR training. IEEE Transactions on Visualization and Computer Graphics, 28(5), 1993–2002. DOI: 10.1109/TVCG.2022.3150510
Liang, H., Liu, S., Wang, Y., Pan, J., Fu, J., & Yuan, Y. (2023). EEG-Based VR Scene Adaptive Generation System for Regulating Emotion. In 2023 9th International Conference on Virtual Reality (ICVR), 361–368. DOI: 10.1109/ICVR57957.2023.10169325
Lin, Y., & Wang, S. (2019). The study and application of adaptive learning method based on virtual reality for engineering education. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11903 LNCS, 372–383. DOI: 10.1007/978-3-030-34113-8_31
Livatino, S., & Hochleitner, C. (2008). Simple Guidelines for Testing VR Applications. DOI: 10.5772/5925
Mariani, A., Pellegrini, E., Enayati, N., Kazanzides, P., Vidotto, M., & de Momi, E. (2018). Design and evaluation of a performance-based adaptive curriculum for robotic surgical training: a pilot study. In 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2162–2165. DOI: 10.1109/EMBC.2018.8512728
Mourning, R., & Tang, Y. (2016). Virtual reality social training for adolescents with high-functioning autism. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4848–4853. DOI: 10.1109/SMC.2016.7844996
Mousavi, S. M. H., Khaertdinov, B., Jeuris, P., Hortal, E., Andreoletti, D., & Giordano, S. (2023). Emotion recognition in adaptive virtual reality settings: Challenges and opportunities. In CEUR-WS Proceedings, 3517, 1–20.
Ojha, A., Narain, S., Raj, A., Agrawal, T., Wadhwa, B., & Joshi, M. (2024). Dynamic virtual reality horror sports enhanced by artificial intelligence and player modeling. Multimedia Tools and Applications. DOI: 10.1007/s11042-024-18414-6
Parsons, T. D. (2021). Ethical challenges of using virtual environments in the assessment and treatment of psychopathological disorders. Journal of Clinical Medicine, 10(3), 378. DOI: 10.3390%2Fjcm10030378
Parsons, T. D., McMahan, T., & Asbee, J. (2024). Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store. Virtual Reality, 28(1). DOI: 10.1007/s10055-023-00927-4
PEDro. (2024). PEDro scale. [link]
Porcino, T., Rodrigues, E. O., Bernardini, F., Trevisan, D., & Clua, E. (2022). Identifying cybersickness causes in virtual reality games using symbolic machine learning algorithms. Entertainment Computing, 41, 100473. DOI: 10.1016/j.entcom.2021.100473
Reidy, L., Chan, D., Nduka, C., & Gunes, H. (2020). Facial electromyography-based adaptive virtual reality gaming for cognitive training. ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction, 174–183. DOI: 10.1145/3382507.3418845
Rovira, A., & Slater, M. (2022). Encouraging bystander helping behaviour in a violent incident: a virtual reality study using reinforcement learning. Scientific Reports, 12(1). DOI: 10.1038/s41598-022-07872-3
Scott, E., Soria, A., & Campo, M. (2016). Adaptive 3D virtual learning environments—A review of the literature. IEEE Transactions on Learning Technologies, 10(3), 262–276. DOI: 10.1109/TLT.2016.2609910
Senno, B., & Barcha, P. (2018). Customizing user experience with adaptive virtual reality. In Companion Proceedings of the 23rd International Conference on Intelligent User Interfaces, Article 42. DOI: 10.1145/3180308.3180351
Siu, K.-C., Best, B. J., Kim, J. W., Oleynikov, D., & Ritter, F. E. (2016). Adaptive virtual reality training to optimize military medical skills acquisition and retention. Military Medicine, 181(5), 214–220. DOI: 10.7205/MILMED-D-15-00164
Stranick, T., & Lopez, C. (2022). Adaptive virtual reality exergame: Promoting physical activity among workers. Journal of Computing and Information Science in Engineering, 22(3). DOI: 10.1115/1.4053002
Uyan, U., & Celikcan, U. (2024). CDMS: A real-time system for EEG-guided cybersickness mitigation through adaptive adjustment of VR content factors. Displays, 83, 102704. DOI: 10.1016/j.displa.2024.102704
Wood, K., Quevedo, A. J. U., Penuela, L., Perera, S., & Kapralos, B. (2021). Virtual reality assessment and customization using physiological measures: A literature analysis. In Proceedings of the 23rd Symposium on Virtual and Augmented Reality, 64–73. DOI: 10.1145/3488162.3488228
Wu, F., & Rosenberg, E. S. (2022). Adaptive field-of-view restriction: Limiting optical flow to mitigate cybersickness in virtual reality. In Proceedings of the 28th ACM Symposium on Virtual Reality Software and Technology, 1–11. DOI: 10.1145/3562939.3565611
Yang, X., Wang, D., & Zhang, Y. (2016). An adaptive strategy for an immersive visuo-haptic attention training game. In Haptics: Perception, Devices, Control, and Applications: 10th International Conference, EuroHaptics 2016, Proceedings, Part I 10, 441–451. DOI: 10.1007/978-3-319-42321-0_41
Yu, S.-J., Hsueh, Y.-L., Sun, J. C.-Y., & Liu, H.-Z. (2021). Developing an intelligent virtual reality interactive system based on the ADDIE model for learning pour-over coffee brewing. Computers and Education: Artificial Intelligence, 2. DOI: 10.1016/j.caeai.2021.100030
Zahabi, M., & Abdul Razak, A. M. (2020). Adaptive virtual reality-based training: a systematic literature review and framework. Virtual Reality, 24(4), 725–752. DOI: 10.1007/s10055-020-00434-w
Publicado
30/09/2024
Como Citar
CORREIA, Pedro Henrique Barcha.
Adaptive Virtual Reality Solutions: A Literature Review. In: SIMPÓSIO DE REALIDADE VIRTUAL E AUMENTADA (SVR), 26. , 2024, Manaus/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1-10.
