Segment-embedded frame definition affects the hip joint centre precision during walking
Bibliographic record
Abstract
Due to marker-specific soft tissue artefacts, the choice of the markers defining the segment-embedded frame affects the functional joint centre location, with subsequent error propagation to joint kinematics and kinetics in gait analysis. Our aim was to assess the effect of the number and placement of markers on the precision of the hip joint centre (HJC) location during walking. Twelve markers (2x6) were attached to the pelvis and left thigh of 15 young male subjects. Set-up movements were collected to locate an optimised functional HJC. For all permutations of three from six markers, a HJC was located and subsequently reconstructed in a static trial and during walking. Precision measures with two different definitions of the origin, namely a single maker or their mean-point, and using three, four, five and six were calculated. Finally, marker triads that reduced the variability of the HJC location were determined. Both the number of markers and method for defining the origin significantly affected the HJC precision during static and walking trials. For walking, precision of 39 mm using three markers improved to 5mm using redundant markers and the mean marker position as the segment origin. Markers placed close to the joint gave more consistent results.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".