Communicating and using biomechanical measures through visual cues to optimise safe and effective rowing
Bibliographic record
Abstract
The use of representations of physiological parameters to an athlete and coach during training is becoming increasingly common. Their utility is enhanced when the appropriate data are captured and communicated in real time for the athlete to make training adjustments immediately. The aim of this work was to develop a biofeedback tool for ergometer rowing by creating a data acquisition system, data analysis and interpretation that could be conducted in real time and a feedback system with appropriate cues to the athlete. This 14-year study resulted in a set of measured parameters with inferred correlations between the directly measured parameters acquired during the activity and performance and injury outcome measures. These parameters were represented through a customisable visual display in real time during ergometer training. An athlete and coach open survey was conducted to assess the utility of the biofeedback tool. This survey found that all parties valued the feedback system since it provided a common language to identify body motion and performance parameters in a way that was accessible and meaningful to all parties as well as available during training and coaching. Athletes noted that it helped them to understand body segment motion and its relation to performance, and both coaches and medical staff valued this in enhancing performance and monitoring injury and injury prediction. There was also speculation that the system helps to underpin coaching practice and its translation to the team. The biofeedback tool has been adopted by the British elite rowing squad.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".