Survey of Formal Methods of Hip Joint Center Calculation in Human Studies
Bibliographic record
Abstract
Functional hip joint center (HJC) calculation involves recording movements of femur relative to acetabulum through markers placed on skin around thigh and pelvis. This non-invasive method of finding hip joint center involves either fitting a geometric sphere onto marker trajectories or coordinate transformation techniques which find the point with least movement in local frame with respect to global frame. A survey study by Ehrig et al has evaluated both categories of formal methods through virtual simulation and also contributed another algorithm known as “SCoRE”(Systematic center of rotation estimation). This algorithm gives an accuracy of 0.5 cm with 20 degree range of motion (ROM) and claimed to be most accurate with both segments in motion. This paper reviews the studies using this method to calculate hip joint center. Also a review of studies using Ultrasound as a validation method has been provided. This forms the basis to the possibility of using Ultrasonic sensors to be placed along with markers to measure the relative movement of markers with respect to bone in vivo. This paper provides a survey of studies performed on human subjects either in vivo (live humans) or ex vivo (cadaver) to help an experimenter or researcher pick the best relevant technique matching their experimentation requirement including soft tissue artifact factor.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".