Development of a New and Mechanically Intelligent Anti-Tremor Utensil
Bibliographic record
Abstract
People living with Parkinson’s disease or with essential tremors face many obstacles in their everyday lives. Being able to eat independently is one of them. Many technologies already exist to help people who have difficulty eating independently. However, following a review of existing devices with a team of occupational therapists, it was found that many commercially available solutions were either unhelpful or too expensive. The need for better adapted solutions was obvious so an iterative design methodology based on the user's needs was followed to create a new anti-tremor utensil. The starting point of the design was to analyze the existing utensils to understand better the pros and cons of the available solutions. During the iterative design methodology, several prototypes emerged and led to the creation of the final spoon prototype presented in this paper. A total of 5 different adaptative spoons were designed and are presented in this paper. A sensor-based frequential analysis combined with an occupational therapist review indicates that the proposed prototype is effective against certain types of tremors and that it could potentially help people living with tremor while they eat. The next step of the development will be to test the new prototype with potential users.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".