Joint Excursion, Handle Velocity, and Applied Force: a Biomechanical Analysis of Ergonometric Rowing
Bibliographic record
Abstract
Rowers may sacrifice on-water technique during ergonometric training in an attempt to increase stroke output. This cross-sectional study aimed to identify characteristics of ergonometric rowing technique that could be potentially detrimental to an effective and safe on-water performance. Joint excursion, handle velocity, and applied force were measured in 44 athletes while they performed a 2500 meter race on an instrumented ergometer. Results on four subjects are presented here. Their performance is compared to that of a Barcelona Olympic and World champion rower with 12 years of experience to illustrate how athletes deviate from standard on-water technique. Kinematic data showed knee joint oscillations and out-of-phase hip and knee joint reversals. Horizontal handle velocity curves indicated that higher stroke rates were achieved by a decrease in recovery time. Vertical handle velocity curves exhibited bi-directional variations. The largest amplitude occurred at the end of the drive phase during an upward displacement of the handle that was associated with a jerk in the applied force. Force-time curves at different stroke rates showed greater variability in the initial portion of the drive phase. Perpetuation of these technique deviations may be detrimental to on-water performance. Biomechanical analyses may allow coaches to better monitor technique during ergonometric training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".