Comparing three generic musculoskeletal models to estimate the tibiofemoral reaction forces during gait and sit-to-stand tasks
Bibliographic record
Abstract
The choice of musculoskeletal (MSK) model is crucial for performing MSK estimations to evaluate muscle demands and joint forces. This study compared two previously published generic MSK models and a modified model to estimate tibiofemoral reaction forces (TFRF) during gait, sit-to-stand, and stand-to-sit. The estimated tibiofemoral reaction forces were compared with an in vivo dataset from six patients using an instrumented knee prosthesis. A correlation and root mean square error (RMSE) in the time-series analysis and relative peak error (RPE) were evaluated. The results showed that the three MSK models were similar in estimating the vertical forces, with a large correlation, and RPE was found around 20 % during gait. The RMSE and the RPE indicated that the modified model had lower total and lateral compartment forces errors for sit-to-stand and stand-to-sit, showing the best performance. The shear forces for all tasks and models showed significant errors. Future MSK studies should consider these findings when researching functional tasks. The modified model was found to be more effective in estimating the vertical tibiofemoral joint reaction forces in tasks that impose greater demands on muscle forces and require high knee and hip flexion.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".