Rethinking VR Input: Co-design with Gamers with Upper Limb Motor Impairments and VR Designers
Bibliographic record
Abstract
Virtual Reality (VR) offers immersive gaming experiences, yet accessibility for gamers with upper limb motor impairments (ULMI) remains underexplored, particularly regarding interaction methods.This thesis investigates the challenges these gamers face and identifies their needs for accessible VR input.First, we conducted an online survey to understand the experiences of these gamers in VR gaming, focusing on the interaction difficulties they encounter and the design limitations of VR controllers.Then, we conducted co-design workshops involving gamers with ULMI and VR designers to gain a comprehensive understanding of accessibility challenges and how these gamers currently engage with games, leading to the co-creation of design solutions for more accessible VR interactions.Finally, we weave findings of both phases to present design considerations as actionable items.These contributions are valuable for guiding the development of more inclusive VR interactions, so that gamers with ULMI can fully participate in the immersive potential of VR.I want to thank my co-supervisors Dr. Audrey Girouard and Dr. Robert Teather for their guidance and teachings of academic research and invaluable feedback throughout my master's journey.I am immensely grateful for your patience and persistent belief in my abilities, even when I was struggling to deliver.Thank you Dr. Girouard for giving me the opportunity to be a part of the READi program, an experience that first introduced me to accessibility and shaped the path of my research.Thank you, Dr
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".