Impact of Upper Body Mass Scaling on Musculoskeletal Model Predictions during Gait
Bibliographic record
Abstract
Utilizing musculoskeletal modeling through an inverse dynamics approach for gait assessment offers a non-invasive method to compute internal joint kinetics and ground reaction forces and moments solely from kinematic data, reducing reliance on cumbersome equipment. The effectiveness of these models relies on the scaling approach adopted to tailor the model to individual subject data. While constant percentage-based, also called uniform scaling-based, has traditionally been used, recently developed upper body shape-based mass distribution approach which accounts for inter-subject inherent mass distribution variation within the same body mass index category, has demonstrated sensitivity of muscle forces and joint kinetics during static posture to segmental masses and centers of mass variation. This study investigates the influence of upper body mass distribution on internal and external kinetics computed using a full body musculoskeletal model during level walking in normal-weight healthy individuals. The findings reveal that variations in segmental masses and centers of mass resulting from different mass scaling approaches significantly alters ground reaction force prediction, especially the vertical component, followed by the medio-lateral and antero-posterior components. Joint reaction forces also show sensitivity to variations in personalized mass distribution, particularly the vertical component at the hip, knee, and ankle joints, followed by the medio-lateral and antero-posterior components. These results emphasize the importance of caution when employing subject-specific upper body musculoskeletal models with uniform mass scaling for gait kinetics assessment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".