Adapting to the User: A Systematic Review of Personalized Interaction in VR

2025-10-14

AI Summary

Existing VR surveys typically examine user state awareness or adaptive interaction in isolation, lacking a systematic integration of their synergistic mechanisms. This paper addresses this gap through a systematic literature review, proposing a unified five-stage conceptual framework that integrates cross-domain adaptive mechanisms. The framework emphasizes real-time inference of user states—driven by multimodal sensing (e.g., physiological signals, behavioral cues, and eye-tracking)—and responsive environmental adaptation. Key trends and challenges identified include trade-offs among multimodal fusion modeling, implicit state recognition, response latency, and interaction transparency. The study clarifies the research trajectory and practical paradigms for personalized VR interaction, establishing a theoretical foundation and scalable methodological pathway for developing highly engaging, expressive, and user-centered adaptive VR systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

As virtual reality (VR) systems become increasingly more advanced, they are likewise expected to respond intelligently and adapt to individual user states, abilities, and preferences. Recent work has explored how VR can be adapted and tailored to individual users. However, existing reviews tend to address either user-state sensing or adaptive interaction design in isolation, limiting our understanding of their combined implementation in VR. Therefore, in this paper, we examine the growing research on personalized interaction in VR, with a particular focus on utilizing participants' immersion information and adaptation mechanisms to modify virtual environments and enhance engagement, performance, or a specific goal. We synthesize findings from studies that employ adaptive techniques across diverse application domains and summarize a five-stage conceptual framework that unifies adaptive mechanisms across domains. Our analysis reveals emerging trends, including the integration of multimodal sensors, an increasing reliance on user state inference, and the challenge of balancing responsiveness with transparency. We conclude by proposing future directions for developing more user-centered VR systems.

Problem

• Reviewing personalized VR interaction combining sensing and adaptation
• Developing unified framework for adaptive mechanisms across domains
• Addressing challenges in multimodal sensor integration and transparency

Innovation

• Personalized VR interaction using user state sensing
• Adaptive mechanisms modify virtual environments for engagement
• Multimodal sensors and user state inference integration