Rhythmic serious games as an inclusive tool for music‐based interventions
Bibliographic record
Abstract
Technologies, such as mobile devices or sets of connected sensors, provide new and engaging opportunities to devise music-based interventions. Among the different technological options, serious games offer a valuable alternative. Serious games can engage multisensory processes, creating a rich, rewarding, and motivating rehabilitation setting. Moreover, they can be targeted to specific musical features, such as pitch production or synchronization to a beat. Because serious games are typically low cost and enjoy wide access, they are inclusive tools perfectly suited for remote at-home interventions using music in various patient populations and environments. The focus of this article is in particular on the use of rhythmic serious games for training auditory-motor synchronization. After reviewing the existing rhythmic games, initial evidence from a recent proof-of-concept study in Parkinson's disease is provided. It is shown that rhythmic video games using finger tapping can be used with success as an at-home protocol, and bring about beneficial effects on motor performance in patients. The use and benefits of rhythmic serious games can extend beyond the rehabilitation of patients with movement disorders, such as to neurodevelopmental disorders, including dyslexia and autism spectrum disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".