Kinematic Analysis of the Lower Extremities of Subjects with Flat Feet at Different Gait Speeds
Bibliographic record
Abstract
[Purpose] This study determined the difference between flat feet and normal feet of humans at different gait velocities using electromyography (EMG) and foot pressure analysis. [Subjects] This study was conducted on 30 adults having normal feet (N = 15) and flat feet (N = 15), all of whom were 21 to 30 years old and had no neurological history or gait problems. [Methods] A treadmill (AC5000M, SCIFIT, UK) was used to analyze kinematic features during gait. These features were analyzed at slow, normal, and fast gait velocities. A surface electromyogram (TeleMyo 2400T, Noraxon Co., USA) and a foot pressure analyzer (FSA, Vista Medical, Canada) were used to measure muscle activity changes and foot pressure, respectively. [Results] The activities of most muscles of the flat feet, except that of the rectus femoris, were significantly different from the muscle activities of the normal feet at different gait velocities. For example, there was a significant difference in the vastus medialis and abductor hallucis muscle. Likewise, flat feet and normal feet showed significant differences in pressures on the forefoot, midfoot, and medial area of the hindfoot at different gait velocities. Finally, comparison showed there were significant differences in pressures on the 2nd-3rd metatarsal area. [Conclusion] Because muscle activation has a tendency to increase with an increase in gait velocity, we hypothesized that the lower extremity with a flat foot requires more work to move due to the lack of a medial longitudinal arch, and consequently pressure was focused on the 2nd-3rd metatarsal area during the stance phase.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".