How Different Marker Sets Affect Joint Angles in Inverse Kinematics Framework
Bibliographic record
Abstract
The choice of marker set is a source of variability in motion analysis. Studies exist which assess the performance of marker sets when direct kinematics is used, but these results cannot be extrapolated to the inverse kinematic framework. Therefore, the purpose of this study was to examine the sensitivity of kinematic outcomes to inter-marker set variability in an inverse kinematic framework. The compared marker sets were plug-in-gait, University of Ottawa motion analysis model and a three-marker-cluster marker set. Walking trials of 12 participants were processed in opensim. The coefficient of multiple correlations was very good for sagittal (>0.99) and transverse (>0.92) plane angles, but worsened for the transverse plane (0.72). Absolute reliability indices are also provided for comparison among studies: minimum detectable change values ranged from 3 deg for the hip sagittal range of motion to 16.6 deg of the hip transverse range of motion. Ranges of motion of hip and knee abduction/adduction angles and hip and ankle rotations were significantly different among the three marker configurations (P < 0.001), with plug-in-gait producing larger ranges of motion. Although the same model was used for all the marker sets, the resulting minimum detectable changes were high and clinically relevant, which warns for caution when comparing studies that use different marker configurations, especially if they differ in the joint-defining markers.
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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 | 0.017 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".