Adaptive Soft Switches: Co-Designing Fabric Adaptive Switches with Occupational Therapists for Children and Adolescents with Acquired Brain Injury
Bibliographic record
Abstract
Acquired brain injuries have many complexities, largely affecting motor and cognitive functioning. Occupational therapists often use switches attached to electronics that activate the devices to give people with disabilities the ability to interact with toys and electronics. However, current switches on the market are expensive, break easily and are unable to customize. We ran two co-design workshops and follow-up interviews with 14 occupational therapists specializing in students with acquired brain injuries. In phase one, the occupation therapists built three soft switches and brainstormed iterations. In phase two, we gained valuable insights into the iterations from occupational therapists. This paper contributes to Human-Computer Interaction as a case study, designs guidelines to support co-design with occupational therapists, and discusses the potential of adaptive soft switches. This work contributes to the growing literature around supporting occupational therapists as makers and how researchers can support them during the co-design process.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".