Validation of a Multi-Segment Kinematic Foot Model and Optical Motion Capture Using Bi-Planar X-Ray Fluoroscopy and a Markerless RSA Approach
Bibliographic record
Abstract
Gait analysis with optical motion capture typically treats the foot as a single segment, which can measure clinically useful kinematics but is insufficient to measure the kinematics of joint motions within the foot. This study hypothesizes that a four-segment foot model, tracking the hindfoot, midfoot, forefoot, and hallux, can accurately measure intrinsic foot kinematics when validated against the gold standard of fluoroscopic X-ray radiostereometric analysis (RSA) during walking gait. Ten healthy volunteers were tested, with the left foot tracked during the stance phase from heel strike to toe off. The results indicated that the height-to-length ratio of the medial longitudinal arch (MLA) and the transverse plane motion of the hindfoot were the most reliable kinematic measures, showing the best agreement between the optical motion capture and RSA methods. In contrast, the frontal plane motions of the hindfoot and forefoot showed the greatest differences, though these were not statistically significant at p < 0.05. These findings demonstrate that the multi-segment foot model is a valid method for measuring intrinsic foot kinematics in a clinical setting, providing a reliable alternative to more invasive techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".