# Variation in Four Horse Gait Categories Illustrated by Quantitative Analysis With ANALOC-E

## TL;DR

This study uses a noninvasive system to analyze horse gaits, revealing new gait types and showing how quantitative data can improve gait classification accuracy.

## Contribution

The study introduces new gait types and demonstrates the potential of ANALOC-E for accurate gait classification in horses.

## Key findings

- Three parameters explain 98.9% of the variance in gait types in the dataset.
- Two gait types (marcha de centro and marcha trotada) were identified that are not described by previous standards.
- ANALOC-E shows promise for accurate locomotion analysis but requires further validation.

## Abstract

Horse gaits are a trait highly selected and prized in diverse breeds. Meanwhile, gait classification relies mostly on subjective visual observations by evaluators. Noninvasive equipment able to track locomotion in horses and output quantitative gait parameters is not only helpful in evaluating locomotion but also in designating gait types and its variations. Equine locomotion pattern designation based solely on observer evaluation can be subjective; therefore, utilizing tools that provide quantitative results and track individual limb movements, especially during intermediate-speed gaits, can result in increased phenotypic accuracy and better designation. A noninvasive automated locomotion analysis system (ANALOC-E) was used to acquire locomotion parameters in a small yet diverse set of 68 horses of 4 breeds. We aimed to evaluate kinematic variation in horse locomotion patterns for these 68 horses. Analysis demonstrates kinematic variation within breed-designated gaits. We also compared output parameters to previously described standards, as well as assessed principal component scores within the dataset. We illustrate two gait types not described by the previous standards (marcha de centro and marcha trotada) yet recognized by breed designations. Three parameters (lateral support, diagonal support, and triple support) can explain about 98.9% of the variance in gait types in this dataset. Results suggest that ANALOC-E could be used for locomotion analysis, but further validation is necessary to evaluate the accuracy of the system. Noninvasive technologies that encourage natural locomotion and output quantitative biomechanical/kinematic parameters may assist in real-time, accurate, locomotion descriptions.