An Instrumented Equine Shoe for Kinetic Gait Analysis
Bibliographic record
Abstract
Within the field of equine veterinary medicine, the diagnosis, prevention and treatment of lameness is one of the most challenging problems facing practitioners and horse owners. Typically, lameness is evaluated by a veterinarian with a visual exam. However, this method can be prone to bias and it can be difficult to properly evaluate subtle or mild cases. Kinetic methods (such as force plates) rely on analysis of the forces that result in motion to identify lameness. Force plates are considered the gold standard for kinetic gait analysis but are unable to record successive strides. The development of a hoof mountable force measuring device would permit the evaluation of ground reaction forces (GRFs) in the horse’s natural environment and over a variety of gaits. There has been previous development of these devices but they are often heavy and require additional equipment outside of the shoe to collect and store data. This paper details the design and evaluation of a tether-free and self contained equine instrumented horseshoe for objective gait analysis. The device was evaluated at the board and system level. Tests comparing the system to load cell measurements in a materials testing machine and an equine cadaver limb setup had errors of 5-10 %. These results provide some confidence for the use of the piezoresistive sensors and potential in vivo applications of the system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".