A Refined Technique to Calculate Finite Helical Axes From Rigid Body Trackers
Bibliographic record
Abstract
Finite helical axes (FHAs) are a potentially effective tool for joint kinematic analysis. Unfortunately, no straightforward guidelines exist for calculating accurate FHAs using prepackaged six degree-of-freedom (6 DOF) rigid body trackers. Thus, this study aimed to: (1) describe a protocol for calculating FHA parameters from 6 DOF rigid body trackers using the screw matrix and (2) to maximize the number of accurate FHAs generated from a given data set using a moving window analysis. Four Optotrak® Smart Markers were used as the rigid body trackers, two moving and two fixed, at different distances from the hinge joint of a custom-machined jig. 6D OF pose information was generated from 51 static positions of the jig rotated and fixed in 0.5 deg increments up to 25 deg. Output metrics included the FHA direction cosines, the rotation about the FHA, the translation along the axis, and the intercept of the FHA with the plane normal to the jig's hinge joint. FHA metrics were calculated using the relative tracker rotation from the starting position, and using a moving window analysis to define a minimum acceptable rotational displacement between the moving tracker data points. Data analysis found all FHA rotations calculated from the starting position were within 0.15 deg of the prescribed jig rotation. FHA intercepts were most stable when determined using trackers closest to the hinge axis. Increasing the moving window size improved the FHA direction cosines and center of rotation accuracy. Window sizes larger than 2 deg had an intercept deviation of less than 1 mm. Furthermore, compared to the 0 deg window size, the 2 deg window had a 90% improvement in FHA intercept precision while generating almost an equivalent number of FHA axes. This work identified a solution to improve FHA calculations for biomechanical researchers looking to describe changes in 3D joint motion.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".