Changes In Knee Biomechanics With Changes In Gait Speeds
Bibliographic record
Abstract
At present, the variation in three dimensional gait parameters throughout the normal range of walking speeds is not well understood. It is integral to understand the natural dynamic relationship between gait parameters and speed changes for a complete understanding of knee function at varying gait speeds. PURPOSE To evaluate the relationship of 3D knee joint kinematics and kinetics to finite increments of gait speed throughout the normal range of walking speeds. METHODS Gait analysis was performed on 20 participants (10 M; 22.7 ± 3.2 y). Participants performed 5 walking trials at each of 5 walking cadences:1) baseline (self selected cadence), 2) 15% above baseline, 3) 15% below baseline, 4) 30% above baseline; and 5) 30% below baseline. The 3D net forces and net moments were calculated. Regression analyses with forces (anterior posterior, medial lateral, distal proximal) and moments (sagittal, transverse, frontal) were performed to examine changes in 3D forces and moments with cadence. RESULTS In separate models between cadence and selected force curve parameters, medial-lateral forces contributed less (R2=0.27) compared to distal-proximal forces (R2=0.77). Higher correlations in the knee moments were seen with parameters from the sagittal plane (R2=0.68), while the frontal plane contributed less (R2=0.17). CONCLUSIONS Results suggest that force and moment magnitudes vary with changes in cadence and that this relationship is more tightly coupled for the forward and vertical forces and the sagittal plane moments (refer to figure). Faster cadences were associated with higher moments in the sagittal plane as the majority of total knee work is performed in this plane.Figure
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".