Predicting Sagittal Plane Biomechanics That Minimize the Axial Knee Joint Contact Force During Walking
Bibliographic record
Abstract
Both development and progression of knee osteoarthritis have been associated with the loading of the knee joint during walking. We are, therefore, interested in developing strategies for changing walking biomechanics to offload the knee joint without resorting to surgery. In this study, simulations of human walking were performed using a 2D bipedal forward dynamics model. A simulation generated by minimizing the metabolic cost of transport (CoT) resembled data measured from normal human walking. Three simulations targeted at minimizing the peak axial knee joint contact force instead of the CoT reduced the peak force by 12-25% and increased the CoT by 11-14%. The strategies used by the simulations were (1) reduction in gastrocnemius muscle force, (2) avoidance of knee flexion during stance, and (3) reduced stride length. Reduced gastrocnemius force resulted from a combination of changes in activation and changes in the gastrocnemius contractile component kinematics. The simulations that reduced the peak contact force avoided flexing the knee during stance when knee motion was unrestricted and adopted a shorter stride length when the simulated knee motion was penalized if it deviated from the measured human knee motion. A higher metabolic cost in an offloading gait would be detrimental for covering a long distance without fatigue but beneficial for exercise and weight loss. The predicted changes in the peak axial knee joint contact force from the simulations were consistent with estimates of the joint contact force in a human subject who emulated the predicted kinematics. The results demonstrate the potential of using muscle-actuated forward dynamics simulations to predict novel joint offloading interventions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".