Musculoskeletal Loads in Ergometer Rowing
Bibliographic record
Abstract
Rowing ergometers can be found in most gyms and fitness centers, but many people who use them regularly have little or no instruction in rowing technique. It is not known whether nonrowers who regularly practice ergometer rowing are at risk of musculoskeletal problems. This study was done to quantify the differences in kinematics, kinetics, and musculoskeletal loading of competitive rowers and nonrowers during ergometer rowing. An experiment was performed to collect kinematic, external force, and EMG data during er-gometer rowing by 5 university-level competitive rowers and 5 nonrowers. Kinematic and external force data were input to a 3-D whole-body musculo-skeletal model which was used to calculate net joint forces and moments, muscle forces, and joint contact forces. The results showed that competitive rowers and nonrowers are capable of rowing an ergometer with generally similar patterns of kinematics and kinetics; however, there are some potentially important differences in how they use their legs and trunk. The competitive rowers generated higher model quadriceps (vastus) muscle forces and pushed harder against the foot cradle, extending their knees more and their trunks less than the nonrowers during the drive phase. They also had higher contact forces at the knee and higher peak lumbar and knee flexion moments. The ratio of average peak vastus force to average peak erector spinae force in the experienced rowers was 1.52, whereas it was only 1.18 in the nonexperienced rowers.
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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) | 0.002 | 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".