Machine Learning Application for Real-Time Simulator
Authors: 
Azadeh Hadadi, Jean-Rémy Chardonnet, Christophe Guillet, Jivka OvtcharovaAuthors Info & Claims
Pages 1 - 5
Abstract
This paper presents a groundbreaking research initiative that focuses on the development of an intelligent architecture for Adaptive Virtual Reality Systems (AVRS) in immersive virtual environments. The primary objective of this architecture is to enable real-time artificial intelligence training and adapt the virtual environment based on user states or external parameters. In a case study focused on detecting cybersickness, an undesired side effect in immersive virtual environments, we utilized this architecture to train an artificial intelligence model and personalize it for individual users in a driving simulator application. By leveraging the capabilities of this architecture, we can optimize virtual reality experiences for individual users, leading to increased comfort. We evaluated the system’s performance in terms of memory usage, CPU and GPU usage, temperature monitoring, frame rate, and network performance, and our results demonstrated the efficiency of our proposed architecture.
References
[1]
Dayi Bian, Joshua Wade, Amy Swanson, Amy Weitlauf, Zachary Warren, and Nilanjan Sarkar. 2019. Design of a physiology-based adaptive virtual reality driving platform for individuals with ASD. ACM Transactions on Accessible Computing (TACCESS) 12, 1 (2019), 1–24.
[2]
Albert Bifet, Ricard Gavalda, Geoffrey Holmes, and Bernhard Pfahringer. 2023. Machine learning for data streams: with practical examples in MOA. MIT press.
[3]
Jean-Rémy Chardonnet, Mohammad Ali Mirzaei, and Frédéric Mérienne. 2017. Features of the postural sway signal as indicators to estimate and predict visually induced motion sickness in virtual reality. International Journal of Human–Computer Interaction 33, 10 (2017), 771–785.
[4]
Natalia Dużmańska, Paweł Strojny, and Agnieszka Strojny. 2018. Can simulator sickness be avoided? A review on temporal aspects of simulator sickness. Frontiers in psychology 9 (2018), 2132.
[5]
Lawrence Frank, Robert S Kennedy, Robert S Kellogg, Michael E McCauley, and ESSEX CORP ORLANDO FL. 1983. Simulator sickness: A reaction to a transformed perceptual world. 1. scope of the problem. In Proceedings of the Second Symposium of Aviation Psychology, Ohio State University, Columbus OH. 25–28.
[6]
Augusto Garcia-Agundez, Christian Reuter, Hagen Becker, Robert Konrad, Polona Caserman, André Miede, and Stefan Göbel. 2019. Development of a classifier to determine factors causing cybersickness in virtual reality environments. Games for health journal 8, 6 (2019), 439–444.
[7]
Azadeh Hadadi, Jean-Rémy Chardonnet, Christophe Guillet, and Jivka Ovtcharova. 2024. Intelligent Virtual Platform for Real-time Cybersickness Detection and Adaptation. In 2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR). IEEE, 231–235.
[8]
Azadeh Hadadi, Christophe Guillet, Jean-Rémy Chardonnet, Mikhail Langovoy, Yuyang Wang, and Jivka Ovtcharova. 2022. Prediction of cybersickness in virtual environments using topological data analysis and machine learning. Frontiers in Virtual Reality 3 (2022), 973236.
[9]
Rifatul Islam, Samuel Ang, and John Quarles. 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). IEEE, 148–155.
[10]
Robert S Kennedy, Norman E Lane, Kevin S Berbaum, and Michael G Lilienthal. 1993. Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The international journal of aviation psychology 3, 3 (1993), 203–220.
[11]
Behrang Keshavarz and Heiko Hecht. 2011. Validating an efficient method to quantify motion sickness. Human factors 53, 4 (2011), 415–426.
[12]
Hyun K Kim, Jaehyun Park, Yeongcheol Choi, and Mungyeong Choe. 2018. Virtual reality sickness questionnaire (VRSQ): Motion sickness measurement index in a virtual reality environment. Applied ergonomics 69 (2018), 66–73.
[13]
Jinwoo Kim, Woojae Kim, Heeseok Oh, Seongmin Lee, and Sanghoon Lee. 2019. A deep cybersickness predictor based on brain signal analysis for virtual reality contents. In Proceedings of the IEEE/CVF international conference on computer vision. 10580–10589.
[14]
Haoyang Mao, Zhenyu Liu, and Chan Qiu. 2021. Adaptive disassembly sequence planning for VR maintenance training via deep reinforcement learning. The International Journal of Advanced Manufacturing Technology (2021), 1–10.
[15]
Mohammad Ali Mirzaei. 2014. Influence of interaction techniques on vims in virtual environments: estimation et prédiction. Ph. D. Dissertation. Ecole nationale supérieure d’arts et métiers-ENSAM.
[16]
M Ali Mirzaei, Sugeng Prianto, Jean-Rémy Chardonnet, Christian Pere, and Frédéric Mérienne. 2013. New motherwavelet for pattern detection in IR image. In 2013 Visual Communications and Image Processing (VCIP). IEEE, 1–6.
[17]
Yunfang Niu, Danli Wang, Ziwei Wang, Fan Sun, Kang Yue, and Nan Zheng. 2019. User experience evaluation in virtual reality based on subjective feelings and physiological signals. Journal of Imaging Science and Technology 63, 6 (2019), 60413–1.
[18]
Jérémy Plouzeau, Jean-Rémy Chardonnet, and Frédéric Merienne. 2018. Using cybersickness indicators to adapt navigation in virtual reality: a pre-study. In 2018 IEEE conference on virtual reality and 3D user interfaces (VR). IEEE, 661–662.
[19]
Konstantinos Tsiakas, Manfred Huber, and Fillia Makedon. 2015. A multimodal adaptive session manager for physical rehabilitation exercising. In proceedings of the 8th ACM international conference on pervasive technologies related to assistive environments. 1–8.
Index Terms
- Machine Learning Application for Real-Time Simulator
Recommendations
Augmented Reality and Machine Learning in Health: A Systematic Review
ICVARS '23: Proceedings of the 2023 7th International Conference on Virtual and Augmented Reality SimulationsAugmented Reality (AR) is a useful technology for providing an information-rich reality by superimposing digital objects and giving a virtual interpretation of the physical environment. AR has played a key role in reducing cognitive load and the ...
Human-scale haptic interaction with a reactive virtual human in a real-time physics simulator
Theoretical and Practical Computer Applications in EntertainmentIn this article we propose a framework for haptic interaction with a reactive virtual human in a physically simulated virtual world. The user controls an avatar in the virtual world via human-scale haptic interface and interacts with the virtual human ...
The Application of Artificial Intelligence in Mobile Learning
ICSEM '10: Proceedings of the 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization - Volume 01This paper research the application of artificial intelligence in mobile learning. In the first place, this paper analyse the meaning of mobile learning and artificial intelligence, make them clear to people. In the second place, explain the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
336 pages
ISBN:9798400716379
DOI:10.1145/3674029
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 September 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Institute for Information Management in Engineering
- French-German University
Conference
ICMLT 2024
ICMLT 2024: 2024 9th International Conference on Machine Learning Technologies
May 24 - 26, 2024
Oslo, Norway
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 22Total Downloads
- Downloads (Last 12 months)22
- Downloads (Last 6 weeks)10
Reflects downloads up to 27 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format