An outlook in the future of Human-AI interaction
User Modeling for Adaptive Virtual Reality Experiences:Personalization from Behavioral and Physiological Time Series
In my doctoral thesis, I explored the fascinating world of virtual reality (VR) and how it can be personalized using human behavior and body responses
Here’s a brief summary of my work:
I designed VR systems that capture multimodal data from the user’s motion, brain, and heart activity. These systems interpret the user’s responses to virtual environments using machine learning algorithms, which then provide feedback or adaptations to the user. This approach opens up new possibilities for VR applications.
The evidence of personalization in my work is demonstrated through various tests in scenarios such as healthcare, police training, and entertainment. The systems were able to estimate the user’s skill level, emotional state, and preferences based on their behavioral and physiological data. Furthermore, they could enhance the user’s experience and performance by adjusting the VR content accordingly.
However, designing personalized VR systems that use sensitive data from the user’s body and behavior presents both technical and ethical challenges. Despite these challenges, the potential benefits and risks of using such systems in various domains, such as education, health, and entertainment, are immense. Future research should consider the user’s context, goals, and consent when creating VR-based systems.
I look forward to continuing my research in the exciting fields of context-aware systems and human-computer integration.