Sensitivity of forelimb swing phase inverse dynamics to inertial parameter errors
Bibliographic record
Abstract
Estimations of segmental inertial parameters are required for true inverse dynamics calculations during the swing phase of locomotion. This study attempts to quantify the effect of inertial parameter errors on inverse dynamic solutions. Swing phase forelimb net joint moments and powers at the trot (mean +/- s.d 3.03 +/- 0.16 m/s) were calculated with sagittal plane kinematic data from 5 Dutch Warmbloods using inertial parameters based on published regression equations for the breed. Significant peaks in the net moment and power curves for each forelimb joint were identified and measured. Net joint moments and powers were then recalculated after varying the segment mass, location of the segment centre of mass and the mass moment of inertia separately for each of the limb segments. Peak values for the net joint moments and joint powers were determined after each variation, and the percent change in peak value per percent change in inertial parameter was calculated. Segment mass was the most influential parameter, followed by location of the centre of mass. Changes in the mass moment of inertia showed little effect on peak values. The most influential single inertial parameter was the mass of the hoof segment with a mean +/- s.d effect 0.74 +/- 0.22 and 0.69 +/- 0.18 percent peak change per percent parameter change on net joint moments and powers, respectively, across all joints. The results demonstrate the need for an accurate approximation of segment masses during the swing phase, especially the hoof, and the need to account for any additional masses in the model, such as shoes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".