Designing Interactive and Immersive Multimodal Installations for People with Disability
Bibliographic record
Abstract
We developed an end-to-end co-creative methodology for designing interactive and immersive multisensory virtual reality experiences with a particular focus on people with disability. Our method draws on what is called “design thinking” to provide a backbone to our approach. This embraces three stages, an empathic first stage, followed by an ideation phase, during which the thematic context is elaborated, and then an iterative exploration phase during which the initial concept is refined and the implementation is achieved. Furthermore, the “cognitive design” methodology developed by one of us led us to an approach incorporating all sensory modalities, not just the audio and visual modalities (that is, it includes odor, tactile, taste and proprioceptive stimuli), in order to deliver an experience that fully enhances the user’s sense of embodiment, and also led us to place the user’s experience at the heart of the installation. Users participate in the design process through co-design protocols. We showcase the application of this methodology in a detailed way for the construction of an interactive and immersive VR installation for people with disabilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".