Non-invasive Assessment and Compensation of Soft-tissue Artefacts in Hip Joint Kinematics
Bibliographic record
Abstract
Accurate location of the hip joint centre is a necessary component in biomechanical human motion analysis to measure skeletal parameters and describe human motion.In human movement analysis, the hip joint centre can be estimated using functional methods based on the relative motion of the femur to pelvis using reflective markers attached to the skin surface through an optical motion capture system.Determination of the hip joint centre by functional methods suffers inaccuracy due to the soft tissue artefact; this is the relative motion between the markers and the underlying bone due to the muscle and skin deformation.Therefore, one of the main objectives in human movement analysis is the assessment and correction of this artefact.Various studies have described the movement of the soft tissue artefact and minimized it invasively.To solve this issue, we present a non-invasive method to assess and reduce the effect of the soft tissue artefact using optical motion capture data and tissue thickness from ultrasound measurements during flexion, extension, and abduction of the hip joint.Results show that the displacement of markers is non-linear and larger in areas closer to the hip joint.Also, the marker displacements are dependent on the movement type, being relatively larger in abduction movement.The quantification of soft tissue artefacts is used as a basis for a correction procedure for hip joint centre and minimizing the soft tissue artefact effects.Results show that our method for soft tissue artefact assessment and minimization reduces the error in the functional hip joint centre approximately from 13-23mm to 7-14 mm. 4.4Data Analysis .................
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".