Construction of a Serious Games for children with Developmental Coordination Disorder
Bibliographic record
Abstract
Abstract Introduction Serious Games are games with a learning objective that aim to improve motor and cognitive skills by simulating challenges similar to real life. This makes them suitable as they allow the treatment, diagnosis and assessment of health conditions, making it possible to use them in interventions with children who have Developmental Coordination Disorder (DCD). Objective To present the construction of a Serious Game for intervention with children diagnosed/suspected of DCD. Methodology This is an exploratory and descriptive study that proposes the creation of a Serious Game to stimulate the motor performance of children with DCD. It was built by a team made up of three teachers and two students from the Occupational Therapy course, a teacher and a student from the Computer Science course, a teacher and a student from the Design course, from August 2020 to August 2022. Results The game initially planned for the Kinect was adapted to work on computers with webcam recognition, resulting in the game “BRINCOORD”. It includes three playable stages designed to work on performance skills in children with BDD. Conclusion “BRINCOORD” is an innovative initiative that highlights games as a promising therapeutic tool. Although research in this area is developing, there are few studies on the effects of games on improving motor skills in children, especially children with DCD. Given this, it is hoped that “BRINCOORD” can contribute to future evidence on the use of Serious Games in intervention with children with DCD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".