Gamification of Breath Training for Wind Players
Bibliographic record
Abstract
This paper presents the gamification of the Abdominal- Thoracic-Expansion-Measurement prototype wear-able device (ATEMP) and the subsequent study. The ATEMP is a newly developed wearable device for musicians that measures breathing-induced abdominal and ribcage expansion and contraction in real-time. Flute Hero video game was developed as a side-scroller game that uses the ATEMP as player input, and provides real-time biofeedback. The Flute Hero video game study (pretest, post-test, control group study design), gathered metrics from 41 flutists and analyzed whether playing Flute Hero improves breathing technique more so than not playing Flute Hero. All participants performed the pretest and post-test, but only the treatment group played Flute Hero for three weeks between the pretest and post-test. Response to the gaming was positive, participants were keen to play the game and remained engaged throughout the duration of the study. Participants commented that they felt playing Flute Hero positively impacted their breathing technique and sound production. The metrics showed significant improvement in the breath period of the treatment group, but did not show significant improvement in abdominal or thoracic expansion. Further research with changes to the experimental methodology, including longer treatment run time, might garner better results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".