Foot Segment Kinematics During Normal Walking Using a Multisegment Model of the Foot and Ankle Complex
Bibliographic record
Abstract
Gait analysis using optical tracking equipment has been demonstrated to be a clinically useful tool for measuring three-dimensional kinematics and kinetics of the human body. However, in current practice, the foot is treated as a single rigid segment that articulates with the lower leg, meaning the motions of the joints of the foot cannot be measured. A multisegment kinematic model of the foot was developed for use in a gait analysis laboratory. The foot was divided into hindfoot, talus, midfoot, and medial and lateral forefoot segments. Six functional joints were defined: Ankle and subtalar joints, frontal and transverse plane motions of the hindfoot relative to midfoot, supination-pronation twist of the forefoot relative to midfoot, and medial longitudinal arch height-to-length ratio. Twelve asymptomatic subjects were tested during barefoot walking with a six-camera optical stereometric system and passive markers organized in triads. Repeatability of reported motions was tested using coefficients of multiple correlation. Ankle and subtalar joint motions and twisting of the forefoot were most repeatable. Hindfoot motions were least repeatable both within subjects and between subjects. Hindfoot and forefoot pronations in the frontal place were found to coincide with dropping of the medial longitudinal arch between early to midstance, followed by supination and rising of the arch in late stance and swing phase. This multisegment foot model overcomes a major shortcoming in current gait analysis practice-the inability to measure motion within the foot. Such measurements are crucial if gait analysis is to remain relevant in orthopaedic and rehabilitative treatment of the foot and ankle.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".