Description
This research area focuses on the design, application and evaluation of machine learning methods to create behavioral models of users in digital environments.
Our emphasis is the analysis of physiological time-series, motion trajectories, and game-play interactions within virtual reality (VR) and mixed reality (MR) systems, aiming to build models that perform real-time clustering and classification of subjective treats like user skill level, emotional states, or mental workload. We focus on XR technologies as a visualization mediums that provide 3D interactive immersive experience with prospective applications on healthcare (physical and mental rehab), professional training, education, or marketing.
- How do immersive technologies enable new pathways for understanding the context in which a user interacts with a system?
- Can the user’s behavioral and physiological data improve the accuracy of ML models estimating human factors?
- What are the challenges and risks of designing personalized systems that transcend current setups with a 2D-based display, touchscreen, keyboard, and mouse?
Frameworks
Latest publications
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Personalized Feature Importance Ranking for Affect Recognition From Behavioral and Physiological Data
Quintero, Luis,
Fors, Uno,
and Papapetrou, Panagiotis
IEEE Transactions on Games,
2023
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Excite-O-Meter: an Open-Source Unity Plugin to Analyze Heart Activity and Movement Trajectories in Custom VR Environments
Quintero, Luis,
Papapetrou, Panagiotis,
Muñoz, John E.,
Mooij, Jeroen,
and Gaebler, Michael
In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW),
2022
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CS:NO – an Extended Reality Experience for Cyber Security Education
Bernsland, Melina,
Moshfegh, Arvin,
Lindén, Kevin,
Bajin, Stefan,
Quintero, Luis,
Solsona Belenguer, Jordi,
and Rostami, Asreen
In ACM International Conference on Interactive Media Experiences,
2022
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Effective Classification of Head Motion Trajectories in Virtual Reality using Time-Series Methods
Quintero, Luis,
Papapetrou, Panagiotis,
Hollmén, Jaakko,
and Fors, Uno
In IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR),
2021
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Excite-O-Meter: Software Framework to Integrate Heart Activity in Virtual Reality
Quintero, Luis,
Muñoz, John E,
Mooji, Jeroen,
and Gaebler, Michael
In IEEE International Symposium on Mixed and Augmented Reality (ISMAR),
2021
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A Psychophysiological Model of Firearms Training in Police Officers : A Virtual Reality Experiment for Biocybernetic Adaptation
Muñoz, John E,
Quintero, Luis,
Stephens, Chad L,
and Pope, Alan T
Frontiers in Psychology,
2020
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Understanding Research Methodologies when Combining Virtual Reality Technology with Machine Learning Techniques
Quintero, Luis
In International Conference on PErvasive Technologies Related to Assistive Environments (PETRA),
2020
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Implementation of Mobile-Based Real-Time Heart Rate Variability Detection for Personalized Healthcare
Quintero, Luis,
Papapetrou, Panagiotis,
Muñoz, John E.,
and Fors, Uno
In International Conference on Data Mining Workshops (ICDMW),
2019
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Integrating Biocybernetic Adaptation in Virtual Reality Training Concentration and Calmness in Target Shooting
Muñoz, John E,
Pope, Alan T,
and Quintero, Luis
Physiological Computing Systems,
2019
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Open-Source Physiological Computing Framework using Heart Rate Variability in Mobile Virtual Reality Applications
Quintero, Luis,
Papapetrou, Panagiotis,
and Muñoz, John E
In International Conference on Artificial Intelligence and Virtual Reality (AIVR),
2019
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Projects
People
user modelling from time series in digital environments
sequential and temporal data mining, explainability, healthcare applications
probabilistic modeling, environmental sustainability, healthcare applications
