Bibliographic record
Abstract
Traditionally, the mechanical testing of ankle-foot orthoses (AFOs) has been performed with simple limb surrogates, typically with a single axis ankle joint and rigid foot and shank components. Since many current AFO designs allow 3D motion, a surrogate lower limb (SLL) that provides anatomically similar motion in all planes is needed to enable realistic load testing and cyclic testing in a controlled manner. The aim of this thesis was to design, fabricate and test a novel SLL that provides anatomically realistic 3D foot motion, based on a consensus of the passive lower limb range of motion (RoM) found in the literature. The SLL design was inspired by the Rizzoli model, sectioning the lower limb into five segments (shank, hindfoot, midfoot, forefoot, toes). Ball and socket joints were used for the shank-hindfoot, hindfoot-midfoot, and midfoot-forefoot. Forefoot-toes used a hinge-type joint. 3D printed flexible thermoplastic polyurethane (TPU) snap-fit connectors connected the 3D printed nylon foot blocks. A threaded ball stud connected the shank shaft and hindfoot. This shank shaft was surrounded by a 3D printed polylactic acid (PLA) shank cover. The foot was cast in silicone rubber to emulate soft tissue, with a PLA custom mould based on a Össur prosthetic foot cover model. The SLL was successfully designed for easy fabrication using readily available techniques, materials, and components. Only the metal shaft required additional machining. 3D printed components used an affordable 3D printer (Artillery Sidewinder X1), and readily available nylon, PLA, and TPU. Using motion capture testing, SLL foot rotation angles were found to be within standard deviation of mean foot passive rotation angle ranges found in the literature, showing that most joints were within 5° of target maximum rotation angles. With load testing, the SLL was shown to survive static loads representing 1.5 times body weight for a 100 kg individuals and cyclic loads representing normal gait loading for 500,000 cycles.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".