Browsing by Author "Raikwar, Aditya R., author"

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Open Access
Assessing usability of full-body immersion in an interactive virtual reality environment
(Colorado State University. Libraries, 2020) Raikwar, Aditya R., author; Ortega, Francisco R., advisor; Beveridge, Ross, committee member; Stephens, Jaclyn, committee member; Smith, Charles, committee member
Improving immersion and playability has a direct impact on the effectiveness of certain Virtual Reality applications. This project looks at understanding how to develop an immersive soccer application with the intention to measure skills, particularly for the use of assessment and health promotion. This project will show the requirements to create a top-down immersive experience with commodity devices. The particular system serves the simulation of a soccer training environment to evade opponents, pass to teammates, and score goals with the objective of measuring the difficulty of single, double, and triple tasks. It is expected that the performance will go down as the level of tasks increases. This hypothesis is extremely relevant as it provides a system that could serve as an assessment tool for people with concussions to return to play (with an OK by a physician) or to promote exercise to non-athletes. This thesis provides all the necessary steps to explain the high-level details of highly immersive applications while providing a future-path for human-subject experiments.
Embargo
Cooking up a better AR experience: notification design and the liabilities of imperfect cues in augmented reality
(Colorado State University. Libraries, 2024) Raikwar, Aditya R., author; Ortega, Francisco R., advisor; Ray, Indrakshi, committee member; Moraes, Marcia, committee member; Soto, Hortensia, committee member
This dissertation investigates optimizing user experience in Augmented Reality (AR). A virtual cooking environment (ARtisan Bistro) serves as a testbed to explore factors influencing user interaction with AR interfaces. The research starts with notification design, examining strategically placed visual and audio notifications in ARtisan Bistro (Chapter 4). Building on this, Chapter 5 explores optimizing these designs for user awareness and delivering critical information, especially when audio is impractical. This involved exploring visual-only notifications, revealing consistent user performance and attention capture comparable to combined visual-audio notifications (no significant difference found). The research demonstrates that well-designed notifications can significantly improve user experience, but it also raises a crucial question: can users always trust the information presented in AR environments? The possibility of imperfect information delivery underscores the importance of reliable information delivery. Chapter 6 explores the impact of imperfect cues generated by machine learning (ML) on user performance in AR visual search tasks. This research highlights the potential for automation bias when users rely heavily on unreliable cues. By investigating both notification design and the limitations of ML systems for reliable information delivery, this dissertation emphasizes the importance of creating a well-rounded user experience in AR environments. The findings underscore the need for further research on optimizing visual notifications, mitigating automation bias, and ensuring reliable information delivery in AR applications.