A Review on the Relationship Between Sound and Movement in Sports and Rehabilitation
Bibliographic record
Abstract
The role of auditory information on perceptual-motor processes has gained increased interest in sports and psychology research in recent years. Numerous neurobiological and behavioral studies have demonstrated the close interaction between auditory and motor areas of the brain, and the importance of auditory information for movement execution, control, and learning. In applied research, artificially produced acoustic information and real-time auditory information have been implemented in sports and rehabilitation to improve motor performance in athletes, healthy individuals, and patients affected by neurological or movement disorders. However, this research is scattered both across time and scientific disciplines. The aim of this paper is to provide an overview about the interaction between movement and sound and review the current literature regarding the effect of natural movement sounds, movement sonification, and rhythmic auditory information in sports and motor rehabilitation. The focus here is threefold: firstly, we provide an overview of empirical studies using natural movement sounds and movement sonification in sports. Secondly, we review recent clinical and applied studies using rhythmic auditory information and sonification in rehabilitation, addressing in particular studies on Parkinson's disease and stroke. Thirdly, we summarize current evidence regarding the cognitive mechanisms and neural correlates underlying the processing of auditory information during movement execution and its mental representation. The current state of knowledge here reviewed provides evidence of the feasibility and effectiveness of the application of auditory information to improve movement execution, control, and (re)learning in sports and motor rehabilitation. Findings also corroborate the critical role of auditory information in auditory-motor coupling during motor (re)learning and performance, suggesting that this area of clinical and applied research has a large potential that is yet to be fully explored.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".