A Methodology for Generating Virtual Reality Immersion Metrics based on System Variables

Author:

Selzer Matias,Castro Silvia M.

Abstract

Technological advances in recent years have promoted the development of virtual reality systems that have awide variety of hardware and software characteristics, providing varying degrees of immersion. Immersionis an objective property of the virtual reality system that depends on both its hardware and softwarecharacteristics. Virtual reality systems are currently attempting to improve immersion as much as possible.However, there is no metric to measure the level of immersion of a virtual reality system based onits characteristics. To date, the influence of these hardware and software variables on immersion hasonly been considered individually or in small groups. The way these system variables simultaneously affectimmersion has not been analyzed either. In this paper, we propose immersion metrics for virtualreality systems based on their hardware and software variables, as well as the development process that ledto their formulation. From the conducted experiment and the obtained data, we followed a methodology togenerate immersion models based on the variables of the system. The immersion metrics presented in thiswork offer a useful tool in the area of virtual reality and immersive technologies, not only to measurethe immersion of any virtual reality system but also to analyze the relationship and importance of thevariables of these systems.

Publisher

Universidad Nacional de La Plata

Subject

Artificial Intelligence,Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Computer Science (miscellaneous),Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Mind the Gap! Advancing Immersion in Virtual Reality—Factors, Measurement, and Research Opportunities;Proceedings of the Human Factors and Ergonomics Society Annual Meeting;2024-08-30

2. Characterizing the Effects of Adding Virtual and Augmented Reality in Robot-Assisted Training;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

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