Determining the maximum diameter for holes in the shoe without compromising shoe integrity when using a multi-segment foot model
Bibliographic record
Abstract
Measuring individual foot joint motions requires a multi-segment foot model, even when the subject is wearing a shoe. Each foot segment must be tracked with at least three skin-mounted markers, but for these markers to be visible to an optical motion capture system holes or 'windows' must be cut into the structure of the shoe. The holes must be sufficiently large avoiding interfering with the markers, but small enough that they do not compromise the shoe's structural integrity. The objective of this study was to determine the maximum size of hole that could be cut into a running shoe upper without significantly compromising its structural integrity or changing the kinematics of the foot within the shoe. Three shoe designs were tested: (1) neutral cushioning, (2) motion control and (3) stability shoes. Holes were cut progressively larger, with four sizes tested in all. Foot joint motions were measured: (1) hindfoot with respect to midfoot in the frontal plane, (2) forefoot twist with respect to midfoot in the frontal plane, (3) the height-to-length ratio of the medial longitudinal arch and (4) the hallux angle with respect to first metatarsal in the sagittal plane. A single subject performed level walking at her preferred pace in each of the three shoes with ten repetitions for each hole size. The largest hole that did not disrupt shoe integrity was an oval of 1.7cm×2.5cm. The smallest shoe deformations were seen with the motion control shoe. The least change in foot joint motion was forefoot twist in both the neutral shoe and stability shoe for any size hole. This study demonstrates that for a hole smaller than this size, optical motion capture with a cluster-based multi-segment foot model is feasible for measure foot in shoe kinematics in vivo.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".