Co-Designing Programmable Fidgeting Experience with Swarm Robots for Adults with ADHD
Bibliographic record
Abstract
Individuals with ADHD grapple with elevated stress levels, emotional regulation challenges, and difficulty sustaining focus. Fidgeting, a behavior traditionally frowned upon, has been shown to help people with ADHD in concentration, emotional and mental state management, and energy regulation. However, traditional fidgeting devices have limited fixed affordances providing cookie-cutter style fidgeting experience to all despite individual differences. Recognizing the uniqueness of individual fidgeting tendencies, we use small tabletop robots to provide a customizable fidgeting interaction experience and conduct co-design sessions with 16 adults diagnosed with ADHD to explore how they envision their fidgeting interactions being changed with these programmable robots. We examine core elements defining a successful fidgeting interaction with robots, assess the significance of customizability in these interactions and any common trends among participants, and investigate additional advantages that interactions with robots may offer. This research reveals nuanced preferences of adults with ADHD concerning robot-assisted fidgeting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".