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Equine veterinary journal2017; 49(5); 688-691; doi: 10.1111/evj.12669

A simple method for equine kinematic gait event detection.

Abstract: Previous studies have validated methods for determining kinematic gait events using threshold-based techniques. However, a simple method that can be successfully applied to walk, trot and canter is yet to be identified. Objective: To develop a simple kinematic method to identify the timing of hoof contact (hoof-on), peak vertical force and lift off (hoof-off), which can be applied in walk, trot and canter. Methods: In vivo method authentication study. Methods: Horses (n = 3) were ridden in walk, trot and canter down a runway on which four force plates were arranged linearly. Three-dimensional forces were recorded at a sampling rate of 960 Hz and were synchronised with a 10-camera motion analysis system sampling at 120 Hz. Events identified from vertical ground reaction force (GRFz) data were hoof-on (GRFz>50N), peak vertical force (GRFzpeak ) and hoof-off (GRFz<50N). Kinematic identification of hoof-on and hoof-off events was based on sagittal planar angles of the fore- and hindlimbs. Peak metacarpophalangeal (MCP)/metatarsophalangeal (MTP) joint extension was used to assess the time of GRFzpeak . The accuracy (mean) and precision (s.d.) of time differences between kinetic and kinematic events were calculated for fore- and hindlimbs at each gait. Results: Hoof-off was determined with better accuracy (range: -3.94 to 8.33 ms) and precision (range: 5.43-11.39 ms) than hoof-on across all gaits. Peak MCP angle (range: 5.83-19.65 ms) was a more precise representation of GRFzpeak than peak MTP angle (range: 11.49-67.75 ms). Conclusions: The sample size was small and therefore further validation is required. The proposed method was tested on a single surface. Conclusions: This study proposes a simple kinematic method of detecting hoof-on, hoof-off and GRFzpeak at walk, trot and canter. Further work should focus on validating the methodology in a larger number of horses and should extend the method for use on surfaces with varying levels of compliance.
Publication Date: 2017-02-28 PubMed ID: 28117488DOI: 10.1111/evj.12669Google Scholar: Lookup
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  • Journal Article

Summary

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The research paper presents a new kinematic method for identifying gait events — hoof contact, peak vertical force and lift off — in horses walking, trotting and cantering, and provides some initial validation for this approach.

Objective and Methods Used

  • The researchers aimed to develop a simple, practical method for identifying key events in a horse’s gait, specifically hoof contact (hoof-on), peak vertical force and lift off (hoof-off) for horses walking, trotting, and cantering.
  • The method was tested on three horses that were ridden down a runway where four force plates were arranged linearly, and the forces generated were recorded using a 10-camera motion analysis system.
  • The timing of these gait events was calculated by identifying changes in vertical ground reaction force (GRFz), and by examining sagittal planar angles of the fore and hindlimbs of the horse.

Results of the Study

  • The results showed that the method was more accurate and precise in determining the timing of hoof-off than for hoof-on across all three gaits.
  • The peak metacarpophalangeal (MCP) joint extension was found to be a more accurate representation of peak force than the peak metatarsophalangeal (MTP) joint extension.

Conclusions and Future Directions

  • Despite the promising results, the researchers highlighted that the sample size used in the study was small, suggesting a need for further validation with more horses.
  • They also pointed out that they only tested the method on a single surface, and therefore future studies should extend the method for use on different surfaces with varying levels of compliance.
  • The researchers concluded by proposing their simple kinematic method as viable for use in detecting key gait events in horses walking, trotting, and cantering.

Cite This Article

APA
Holt D, St George LB, Clayton HM, Hobbs SJ. (2017). A simple method for equine kinematic gait event detection. Equine Vet J, 49(5), 688-691. https://doi.org/10.1111/evj.12669

Publication

ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 49
Issue: 5
Pages: 688-691

Researcher Affiliations

Holt, D
  • Department of Research, Myerscough College, Preston, UK.
St George, L B
  • Centre for Applied Sport and Exercise Sciences, University of Central Lancashire, Preston, UK.
Clayton, H M
  • Sport Horse Science, Mason, Michigan, USA.
Hobbs, S J
  • Centre for Applied Sport and Exercise Sciences, University of Central Lancashire, Preston, UK.

MeSH Terms

  • Animals
  • Biomechanical Phenomena
  • Forelimb
  • Gait / physiology
  • Hindlimb
  • Hoof and Claw / physiology
  • Horses / physiology
  • Walking

Citations

This article has been cited 4 times.
  1. St George LB, Clayton HM, Sinclair JK, Richards J, Roy SH, Hobbs SJ. Electromyographic and Kinematic Comparison of the Leading and Trailing Fore- and Hindlimbs of Horses during Canter. Animals (Basel) 2023 May 25;13(11).
    doi: 10.3390/ani13111755pubmed: 37889657google scholar: lookup
  2. Davíðsson HB, Rees T, Ólafsdóttir MR, Einarsson H. Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors. Animals (Basel) 2023 Jan 3;13(1).
    doi: 10.3390/ani13010183pubmed: 36611791google scholar: lookup
  3. St George L, Clayton HM, Sinclair J, Richards J, Roy SH, Hobbs SJ. Muscle Function and Kinematics during Submaximal Equine Jumping: What Can Objective Outcomes Tell Us about Athletic Performance Indicators?. Animals (Basel) 2021 Feb 5;11(2).
    doi: 10.3390/ani11020414pubmed: 33562875google scholar: lookup
  4. Serra Bragança FM, Broomé S, Rhodin M, Björnsdóttir S, Gunnarsson V, Voskamp JP, Persson-Sjodin E, Back W, Lindgren G, Novoa-Bravo M, Gmel AI, Roepstorff C, van der Zwaag BJ, Van Weeren PR, Hernlund E. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning. Sci Rep 2020 Oct 20;10(1):17785.
    doi: 10.1038/s41598-020-73215-9pubmed: 33082367google scholar: lookup