Adaptive Interaction Paradigm for Mixed Reality Games

This paper introduces an adaptive interaction paradigm for Mixed Reality (MR) games, designed to enhance accessibility, scalability, and responsiveness in large-scale MR environments. By leveraging depth-sensing technology and real-time 3D skeletal tracking, the paradigm enables virtual elements to dynamically adjust to user movements, creating personalized and inclusive interactions. Unlike traditional fixed interaction models, this approach tailors interaction zones and gesture thresholds to individual user metrics, addressing limitations in current MR designs that fail to accommodate diverse physical abilities. The proposed method employs an egocentric rule-based framework, ensuring low-latency, real-time performance while maintaining transparency and adaptability. Privacy-by-design principles are integral to this approach, with local computation and data anonymization preserving user confidentiality. The effectiveness of this adaptive paradigm is demonstrated through a large-scale MR gameplay use case, with insights from over 5,000 gameplay sessions informing the refinement of interaction models. Beyond gaming, this paradigm establishes a foundation for broader applications in education, rehabilitation, and accessibility technologies, advancing the state of user-centric MR interaction design.