From Imagination to Innovation: Using Participatory Design Fiction to Envision the Future of Accessible Gaming Wearables for Players with Upper Limb Motor Disabilities
Bibliographic record
Abstract
The interest in enhancing video game interactions through wearable technology has grown, yet accessible gaming with wearables remains underexplored. This study employs participatory design fiction, enabling disabled gamers to envision a future with tailored gaming wearables while critiquing technology. We conducted a two-phase study. Phase one involved in-depth interviews with upper limb motor disability participants; we developed a fictitious gaming wearable by analyzing the data using reflexive thematic analysis. A smaller group iterated on the wearable in phase two to ideate on ideal futures with accessible gaming wearables. Using data and dialogic/performance analysis, we crafted a design fiction diegetic prototype as a tech review video. This research highlights disabled gamers' unique needs and experiences around gaming wearables. It offers an innovative diegetic prototype for accessible gaming tech. Our methodological contribution merges narrative inquiry and dialogic/performance analysis in participatory design fiction research, providing a valuable approach for future studies.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".