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Forskningsartikel2021Vetenskapligt granskadÖppen tillgång

Reliable and clinically applicable gait event classification using upper body motion in walking and trotting horses

Roepstorff, Christoffer; Dittmann, Marie Theres; Arpagaus, Samuel; Braganca, Filipe Manuel Serra; Hardeman, Aagje; Persson-Sjodin, Emma; Roepstorff, Lars; Gmel, Annik Imogen; Weishaupt, Michael Andreas


Objectively assessing horse movement symmetry as an adjunctive to the routine lameness evaluation is on the rise with several commercially available systems on the market. Prerequisites for quantifying such symmetries include knowledge of the gait and gait events, such as hoof to ground contact patterns over consecutive strides. Extracting this information in a robust and reliable way is essential to accurately calculate many kinematic variables commonly used in the field. In this study, optical motion capture was used to measure 222 horses of various breeds, performing a total of 82 664 steps in walk and trot under different conditions, including soft, hard and treadmill surfaces as well as moving on a straight line and in circles. Features were extracted from the pelvis and withers vertical movement and from pelvic rotations. The features were then used in a quadratic discriminant analysis to classify gait and to detect if the left/right hind limb was in contact with the ground on a step by step basis. The predictive model achieved 99.98% accuracy on the test data of 120 horses and 21 845 steps, all measured under clinical conditions. One of the benefits of the proposed method is that it does not require the use of limb kinematics making it especially suited for clinical applications where ease of use and minimal error intervention are a priority. Future research could investigate the extension of this functionality to classify other gaits and validating the use of the algorithm for inertial measurement units. (C) 2020 The Authors. Published by Elsevier Ltd.


Equine kinematics; Gait classification; Motion capture; Time frequency analysis; Discriminant analysis

Publicerad i

Journal of Biomechanics
2021, Volym: 114, artikelnummer: 110146