Stride frequency derived from GPS speed fluctuations in galloping horses.
Abstract: Changes in gallop stride parameters prior to injury have been documented previously in Thoroughbred racehorses. Validating solutions for quantification of fundamental stride parameters is important for large scale studies investigating injury related factors. This study describes a fast Fourier transformation-based method for extracting stride frequency (SF) values from speed fluctuations recorded with a standalone GPS-logger suitable for galloping horses. Limits of agreement with SF values derived from inertial measurement unit (IMU) pitch data are presented. Twelve Thoroughbred horses were instrumented with a GPS-logger (Vbox sport, Racelogic, 10 Hz samplerate) and a IMU-logger (Xsens DOT, Xsens, 120 Hz samplerate), both attached to the saddlecloth in the midline caudal to the saddle and time synchronized by minimizing root mean square error between differentiated GPS and IMU heading. Each horse performed three gallop trials with a target speed of 36miles per hour (16.1 ms) on a dirt racetrack. Average speed was 16.48 ms ranging from 16.1 to 17.4 ms between horses. Limits of agreement between GPS- and IMU-derived SF had a bias of 0.0032 Hz and a sample-by-sample precision of +/-0.027 Hz calculated over N = 2196 values. The stride length uncertainty related to the trial-by-trial SF precision of 0.0091 Hz achieved across 100 m gallop sections is smaller than the 10 cm decrease in stride length that has been associated with an increased risk of musculoskeletal injury. This suggests that the described method is suitable for calculating fundamental stride parameters in the context of injury prevention in galloping horses.
Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.
Publication Date: 2022-11-01 PubMed ID: 36343415DOI: 10.1016/j.jbiomech.2022.111364Google Scholar: Lookup
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- Journal Article
Summary
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This study discusses a technique for measuring the stride frequency (SF) of galloping horses using GPS speed fluctuations, which can be used to identify changes that may precede injuries. The method relies on Fourier transformation and has shown good agreement with values derived from pitch data collected through an inertial measurement unit (IMU).
Objective and Importance of the Research
- This study aims to introduce and validate a method for calculating the stride frequency (SF) of galloping horses from speed variations recorded by a standalone GPS sensor.
- The research’s importance arises from the need to identify changes in galloping parameters that could signal an impending injury in racehorses. This method offers a practical tool for large-scale studies that investigate injury-related factors.
Methodology and Experiment
- Twelve Thoroughbred horses were equipped with both a standalone GPS-logger (sampling at a rate of 10 Hz) and an inertial measurement unit (IMU) logger (sampling at 120 Hz). These devices were attached to the saddlecloth on the horse’s mid-back and were time-synchronized by minimizing differences between the GPS and the IMU heading data.
- Each horse then performed three gallop trials at a target speed of 16.1 m/s (approximately 36 miles per hour) on a dirt racetrack.
Outcome and Conclusion
- The study found an average speed of around 16.48 m/s, with differing speeds between horses ranging from 16.1 to 17.4 m/s.
- The limits of agreement between the stride frequencies determined by the GPS and the IMU data showed a bias of 0.0032 Hz and a sample-by-sample precision of +/- 0.027 Hz across all collected values.
- The uncertainty in stride length associated with the SF precision of 0.0091 Hz (over 100 m gallop sections) was found to be smaller than the stride length decrease of 10 cm, previously associated with an increased risk of musculoskeletal injury.
- From these outcomes, the study concludes that the methods described are suitable for determining fundamental stride parameters that can be vital in preventing injuries with galloping horses.
Cite This Article
APA
Pfau T, Bruce O, Brent Edwards W, Leguillette R.
(2022).
Stride frequency derived from GPS speed fluctuations in galloping horses.
J Biomech, 145, 111364.
https://doi.org/10.1016/j.jbiomech.2022.111364 Publication
Researcher Affiliations
- Faculty of Kinesiology, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada; Faculty of Veterinary Medicine, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada. Electronic address: thilo.pfau@ucalgary.ca.
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada.
- Faculty of Kinesiology, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada.
- Faculty of Veterinary Medicine, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada.
MeSH Terms
- Horses
- Animals
- Sports
Conflict of Interest Statement
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: TP is co-owner of EquiGait Ltd, a company providing equine gait analysis products and services. The remaining authors have no conflicts of interest to declare.
Citations
This article has been cited 1 times.- 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).
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