An In-School-Based Program of Combined Fine Motor Exercise and Educational Activities for Children with Neurodevelopmental Disorders
Bibliographic record
Abstract
This article introduces a game-based rehabilitation platform designed to integrate training of fine motor skills and cognitive functions. A novel computer interface device was developed that can effectively replace a standard computer mouse when doing exercises to rehabilitate hand function. This smart device converts signals from miniature motion sensors to signals equivalent to that of a computer mouse. In this way, nearly any object or utensil can be changed to function exactly as a computer mouse, simply by attaching the motion sensor. Multiple objects with varied sizes, shapes, weights, and functional demands for precision can be used for exercise and to practice a variety of gross or fine motor skills, and, importantly, while playing fun computer games. The platform was designed to work with modern and common computer games, which have a broad range of movement speeds and accuracy levels, cognitive activities (puzzles, choices, distractors), and educational content. The platform includes a designed assessment game with advanced data logging for electronic monitoring. Data analysis methods have been developed to quantify performance metrics that provide insights into the quality, efficiency, and skill of a child and thus mean to conduct trend analyses that indicate how the child is performing over time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".