Musculoskeletal model predictions sensitivity to upper body mass scaling during gait
Bibliographic record
Abstract
Musculoskeletal modeling based on inverse dynamics provides a cost-effective non-invasive means for calculating intersegmental joint reaction forces and moments, solely relying on kinematic data, easily obtained from smart wearables. On the other hand, the accuracy and precision of such models strongly hinge upon the selected scaling methodology tailored to subject-specific data. This study investigates the impact of upper body mass distribution on internal and external kinetics computed using a comprehensive musculoskeletal model during level walking in both normal weight and obese individuals. Human motion data was collected using seventeen body worn inertial measuring units for nineteen (19) healthy subjects. The results indicate that variations in segmental masses and centers of mass, resulting from diverse mass scaling techniques, significantly affect ground reaction force estimations in obese subjects, particularly in the vertical component, with a root mean square error (RMSE) of 54.7 ± 23.8 %BW; followed by 12.3 ± 8.0 %BW (medio-lateral); and 6.2 ± 3.2 %BW (antero-posterior). The vertical component of hip, knee, and ankle joint reaction forces also exhibit sensitivity to personalized mass distribution variations. Importantly, the degree of deviation in model predictions increases with body mass index. Statistical analysis using single sample Wilcoxon-Signed Rank test for non-normal data and t -test for normal data, revealed significant differences (p < 0.05) in the computed errors in kinetic parameters between the two scaling approaches. The body shape-based scaling approach significantly impacts musculoskeletal modeling in clinical applications where the upper body mass distribution is crucial, such as in spinal deformities, obesity, and low back pain. This approach accounts for the body shape inherent variability within the same BMI category and enhances the predicted joint kinetics. • Impact of upper body mass distribution on musculoskeletal model predictions during gait. • Two mass scaling methods: Constant Percentage-Based (CPB) and Body Shape-Based (BSB). • Gait dynamics in both normal-weight and obese individuals using subject-specific models. • BSB scaling approach incorporated fifteen anthropometric measurements, providing a personalized model. • Predicted joint reaction forces and ground reaction forces are highly sensitive to mass scaling methods. • Obese individuals demonstrated increased sensitivity to variations in model scaling compared to normal-weight subjects. • Statistical analysis revealed significant differences in model predictions between CPB and BSB methods. • Results highlight the need for personalized scaling to improve the accuracy of musculoskeletal models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".