Evaluation of Eight Methods for Aligning Orientation of Two Coordinate Systems
Bibliographic record
Abstract
The aim of this study was to evaluate eight methods for aligning the orientation of two different local coordinate systems. Alignment is very important when combining two different systems of motion analysis. Two of the methods were developed specifically for biomechanical studies, and because there have been at least three decades of algorithm development in robotics, it was decided to include six methods from this field. To compare these methods, an Xsens sensor and two Optotrak clusters were attached to a Plexiglas plate. The first optical marker cluster was fixed on the sensor and 20 trials were recorded. The error of alignment was calculated for each trial, and the mean, the standard deviation, and the maximum values of this error over all trials were reported. One-way repeated measures analysis of variance revealed that the alignment error differed significantly across the eight methods. Post-hoc tests showed that the alignment error from the methods based on angular velocities was significantly lower than for the other methods. The method using angular velocities performed the best, with an average error of 0.17 ± 0.08 deg. We therefore recommend this method, which is easy to perform and provides accurate alignment.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".