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An Instrumented Equine Shoe for Kinetic Gait Analysis

2023· article· en· W4390993417 on OpenAlexafffund
Alanna Devolin, Ifaz T. Haider, Olivia Kenny, W. Brent Edwards, Kartikeya Murari, W. M. Scott

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsHotchkiss Brain InstituteAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersCalgary Foundation
KeywordsGait analysisGaitKinetic energyComputer sciencePhysical medicine and rehabilitationPhysicsMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.178
GPT teacher head0.467
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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