Limitations of Functionally Determined Joint Centers for the Analysis of Athletic Human Movement: A Case Study of the Upper Limb
Bibliographic record
Abstract
Much research is ongoing into improving the accuracy of functional algorithms to determine joint centers (JC), but there has been limited testing using human movement data. This paper is in three parts: Part 1, errors in determining JCs from real human movement data using the SCoRE method; Part 2, variability of marker combinations during a punch; Part 3, variability in the JC due to reconstruction. Results indicate determining the JC of the shoulder or elbow with a triad of markers per segment with an accuracy greater than 20 mm is unlikely. Part 2 suggests conducting a pilot study with abundant markers to obtain triads, which are most stable due to differences of 300-400% in variability between triads. Variability due to the choice of reference frame for reconstruction during the punch ranged from 2.5 to 13.8 mm for the shoulder and 1.5 to 21.1 mm for the elbow. It would appear more pertinent to enhance the practical methods in situ than to further improve theoretical accuracy of functional methods.
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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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".