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Data in brief2024; 55; 110764; doi: 10.1016/j.dib.2024.110764

Inertial sensor data of horses from four breeds at walk and trot in hand on a straight line.

Abstract: Horses have been used and bred for centuries for their movements. However, specific breeds are expected to have different movement capabilities. We have measured 425 horses from four different breeds at walk and trot on a straight line using an inertial measurement unit (IMU) system (EquiMoves®). This article describes how the data was collected, filtered and analysed to provide a useable dataset of 28 movement variables. It provides a full protocol for field measurements and requirements for adequate trials for analysis. Intra-class correlation coefficient estimates are also provided to assess repeatability of the measurements.
Publication Date: 2024-07-22 PubMed ID: 39183964PubMed Central: PMC11342895DOI: 10.1016/j.dib.2024.110764Google Scholar: Lookup
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  • Journal Article

Summary

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The study measures the movement capabilities of 425 horses from four different breeds to create a dataset of 28 movement variables using an inertial measurement unit (IMU) system. The article describes the data collection, filtering, and analysis process.

Data Collection

  • The authors used an Inertial Measurement Unit (IMU) system called EquiMoves® to measure the movement of the horses. Inertial Measurement Units are devices that measure velocity, orientation and gravitational forces, often used in navigation or mobility assessments.
  • Each horse was measured while walking and trotting on a straight line, to create a uniform measurement environment for all horses.
  • The horses included in the study represented four different breeds, to provide a diverse sample for analysis.

Data Analysis

  • The data collected was passed through a series of filtering and analysis processes to extract meaningful information. This included 28 different movement variables, which potentially could represent different aspects of the horses’ gait or movement patterns.
  • The paper gives a full protocol for the analysis process, allowing other researchers to reproduce the methods in their own studies. This reproducibility is a key part of scientific research, ensuring that results can be verified by independent researchers.

Reliability and Accuracy

  • The authors used estimates of the intra-class correlation coefficient to assess the reliability of the measurements. This statistical measure is commonly used to assess how consistently different measurements of the same variable agree with each other.
  • By including this measure in the paper, the authors show that their data collection method is not only repeatable, but that it also produces consistent results across multiple measurements. This gives readers confidence in the quality and accuracy of the data.

Implications and Applications

  • With these measurements, data can be compared across breeds, and specific movement capabilities of different breeds can be explored. These findings could be relevant for everything from horse breeders, to trainers, to veterinarians, helping improve understanding of horse movement and potentially leading to improved training or health interventions for different breeds.
  • Moreover, this could provide a foundation for further research into the movement capabilities of horses, particularly in relation to breed-specific characteristics or conditions.

Cite This Article

APA
Gmel AI, Haraldsdóttir EH, Serra-Bragança F, Lamas LP, Rosa TV, Stefaniuk-Szmukier M, Klecel W, Neuditschko M, Weishaupt MA. (2024). Inertial sensor data of horses from four breeds at walk and trot in hand on a straight line. Data Brief, 55, 110764. https://doi.org/10.1016/j.dib.2024.110764

Publication

ISSN: 2352-3409
NlmUniqueID: 101654995
Country: Netherlands
Language: English
Volume: 55
Pages: 110764

Researcher Affiliations

Gmel, Annik Imogen
  • Agroscope, Animal GenoPhenomics, Route de la Tioleyre 4, 1725 Posieux, Switzerland.
  • Equine Department, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, 8057 Zurich, Switzerland.
Haraldsdóttir, Eyrún Halla
  • Agroscope, Animal GenoPhenomics, Route de la Tioleyre 4, 1725 Posieux, Switzerland.
Serra-Bragança, Filipe
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 114, 3584 CM, Utrecht, the Netherlands.
Lamas, Luis P
  • CIISA, Faculdade de Medicina Veterináriada Universidade de Lisboa, Lisboa, Portugal.
Rosa, Teresa V
  • CIISA, Faculdade de Medicina Veterináriada Universidade de Lisboa, Lisboa, Portugal.
Stefaniuk-Szmukier, Monika
  • National Research Institute of Animal Production, University of Agriculture in Krakow, Krakow, Poland.
Klecel, Weronika
  • Department of Animal Genetics and Conservation, Institute of Animal Sciences, Warsaw University of Life Sciences, Warsaw, Poland.
Neuditschko, Markus
  • Equine Department, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, 8057 Zurich, Switzerland.
Weishaupt, Michael A
  • Agroscope, Animal GenoPhenomics, Route de la Tioleyre 4, 1725 Posieux, Switzerland.

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