Equations to Prescribe Bicycle Saddle Height based on Desired Joint Kinematics and Bicycle Geometry
Bibliographic record
Abstract
ABSTRACT Overuse knee injuries are common in bicycling and are often attributed to poor bicycle‐fit. Bicycle‐fit for knee health focuses on setting saddle height to elicit a minimum knee flexion angle of 25‐40°. Equations to predict saddle height include a single input, resulting in a likely suboptimal bicycle‐fit. The purpose of this work was to develop an equation to predict saddle height from anthropometrics, bicycle geometry, and user‐defined joint kinematics. Methods: Forty healthy adults (17 women, 23 men; mean (SD): 28.6 (7.2) years; 24.2 (2.6) kg/m 2 ) participated. Kinematic analyses were conducted for 18 three‐minute bicycling bouts including all combinations of 3 horizontal and 3 vertical saddle positions, and 2 crank arm lengths. For both minimum and maximum knee flexion, predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and final models were fit using linear regression. Secondary analyses determined if saddle height equations were sex dependent. Results: The equation to predict saddle position from minimum knee flexion angle (R 2 =0.97; root mean squared error (RMSE) = 1.15 cm) was: Saddle height (cm) = 7.41 + 0.82(inseam cm) – 0.1(minimum knee flexion °) + 0.003(inseam cm)(seat tube angle °). The maximum knee flexion equation (R 2 =0.97; RMSE=1.15 cm) was: Saddle height (cm) = 41.63 + 0.78(inseam cm) – 0.25(maximum knee flexion °) + 0.002(inseam cm)(seat tube angle °). The saddle height equations were not dependent on sex. Conclusions: These equations provide a novel, practical strategy for bicycle‐fit that accounts for rider anthropometrics, bicycle geometry and user‐defined kinematics. Highlights This work developed simple equations to prescribed bicycle saddle height that elicits desired knee kinematics. Separate equations are presented for prescribing minimum or maximum knee flexion angle. Equations can be generalized to riders of both sexes, and a breadth of anthropometrics and ages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".