Intuitive Navigation in Computer Applications for People with Parkinson’s
Bibliographic record
Abstract
Research is reported concerning the development of intuitive navigation within a music-dance application designed for people with Parkinson’s disease (PD). Following a review of current research into the use of motion-sensing cameras in therapeutic contexts, we describe the method by which a relatively low-cost motion-sensing camera was coupled with a rotatable function menu. The rotatable element of the system enabled navigation of the entire menu using only two, readily performable and distinct gestures: side-swipe for function browsing; down-swipe for function selecting. To aid acceptance amongst the target audience, people with PD (who tend to be relatively elderly), the rotatable function menu was rendered as a ballerina jewelry box, with appropriate texturing and music-box style musical accompaniment. Menu implementation employed a game engine, and a software development kit allowed direct access to the camera’s functionality, enabling gesture recognition. For example, users’ side-swipe gestures spun the jewelry box (as if interacting with an object in the real world) until a desired function, presented on each facet of the box, was forward facing. Also described are features and settings, designed to ensure safe and effective use of the application, and help maintain user motivation and interest.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.010 | 0.003 |
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".