How to compare knee kinetics at different walking speeds?
Bibliographic record
Abstract
BACKGROUND: Walking speed is a confounding factor in biomechanical analyses of gait, but still many studies compare gait biomechanics at comfortable walking speed (CWS) that is likely to differ between groups or conditions. To identify gait deviation unrelated to walking speed, methods are needed to correct biomechanical data over the gait cycle for walking speed. RESEARCH QUESTION: How to compare knee kinetics over the gait cycle at different walking speeds? METHODS: 22 asymptomatic subjects walked on a dual-belt treadmill at CWS and 4 fixed speeds. Knee moments in sagittal (KFM) and frontal plane (KAM) were calculated via inverse dynamics. The net moment differences between CWS and fixed speed were expressed as a root-mean-square error (RMSE) normalized to the range of the variable. Two methods to correct for walking speed were compared. In method 1, KFM and KAM values were estimated based on interpolation between speeds at each percentage of the gait cycle. In method 2, principal component analysis was used to extract speed related features to reconstruct KFM and KAM at the speed of interest. The accuracy of both methods was tested using a leave-one-out cross validation. RESULTS: Walking speed influenced the magnitude and shape of KFM and KAM. To account for these speed influences using both methods, leave-one-out cross validation showed low normalized RMSE (< 5 %), with little difference between the two methods. RMSE for both reconstruction methods were up to 60 % lower than the RMSE between CWS and fixed speed. SIGNIFICANCE: Both methods could accurately correct knee kinetics over the gait cycle for the effects of walking speed. Walking speed dependency should be incorporated in each gait laboratory's reference dataset to be able to identify gait deviations unrelated to gait speed.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".