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Home / Equine Research Bank / Animals (Basel) / The Effect of Rider:Horse Bodyweight Ratio on the Superficia...
Animals : an open access journal from MDPI2022; 12(2); 195; doi: 10.3390/ani12020195

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing.

Abstract: Appropriate matching of rider-horse sizes is becoming an increasingly important issue of riding horses' care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body's surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10-12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.
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Publication Date: 2022-01-13 PubMed ID: 35049815PubMed Central: PMC8772910DOI: 10.3390/ani12020195Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research investigated whether the rider-horse bodyweight ratio affects the body temperature in a horse’s thoracolumbar region. This was determined by using advanced thermal image processing tools. The study established that there are measurable differences across the different weight ratios.

Background

  • The research is premised on the growing concern regarding the right matching of rider-horse sizes as the human population becomes heavier. This is because a mismatch in size can cause discomfort and potential harm to the horse.
  • Previous studies used Infrared Thermography (IRT) to assess the impact of rider:horse bodyweight ratios. However, these methods were not effective in discerning the effects of small increments in weight ratios, bringing forth the need for more sophisticated methods.

Methodology

  • The study employed image texture analysis to assess the pixel relations in the thermal imaging. Three methods used included histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM).
  • The research aimed at understanding the impact of varying the rider:horse bodyweight ratios at three levels: 10-12%, >12 ≤15%, >15 <18%.
  • Twelve horses were ridden by six riders, each categorized into three groups: light (L), moderate (M), and heavy (H). Thermal images of the horses’ thoracolumbar regions were taken before and after a standard exercise.
  • The thermal images were decomposed into red, green, and blue components for detailed texture analysis. The different components were analyzed to identify any temperature changes in the horse owing to changes in the rider’s bodyweight.

Findings

  • Of all the collected data, it was found that 95 HS features, 48 GLRLM features, and 96 GLCM features exhibited variations when related to exercise. Furthermore, 29 HS features, 16 GLRLM features, and 30 GLCM features showed variation when related to bodyweight ratio.
  • Unlike traditional thermal features, texture heterogeneity measures such as InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered. This implies that these tools provide a more nuanced understanding of the horse’s bodily response to changes in the weight ratio.

Conclusion

  • The study thus concludes that using advanced thermal image processing can provide more detailed insights into how the rider-horse bodyweight ratio affects the horse’s body temperature. This can significantly contribute to horse care by helping identify the ideal rider-horse bodyweight ratio, thereby enhancing the comfort and well-being of the horse.

Cite This Article

APA
Domino M, Borowska M, Trojakowska A, Kozłowska N, Zdrojkowski Ł, Jasiński T, Smyth G, Maśko M. (2022). The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing. Animals (Basel), 12(2), 195. https://doi.org/10.3390/ani12020195

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 12
Issue: 2
PII: 195

Researcher Affiliations

Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Trojakowska, Anna
  • The Scientific Society of Veterinary Medicine Students, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Kozłowska, Natalia
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Zdrojkowski, Łukasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Smyth, Graham
  • Menzies Health Institute Queensland, Griffith University School of Medicine, Southport, QLD 4222, Australia.
Maśko, Małgorzata
  • Department of Animal Breeding, Institute of Animal Science, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.

Grant Funding

  • WI/WM-IIB/2/2021 / Ministry of Science and Higher Education

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 3 times.
  1. Domino M, Borowska M, Zdrojkowski Ł, Jasiński T, Sikorska U, Skibniewski M, Maśko M. Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding.. Sensors (Basel) 2022 Aug 13;22(16).
    doi: 10.3390/s22166052pubmed: 36015813google scholar: lookup
  2. Verdegaal EJMM, Howarth GS, McWhorter TJ, Delesalle CJG. Is Continuous Monitoring of Skin Surface Temperature a Reliable Proxy to Assess the Thermoregulatory Response in Endurance Horses During Field Exercise?. Front Vet Sci 2022;9:894146.
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  3. Domino M, Borowska M, Kozłowska N, Trojakowska A, Zdrojkowski Ł, Jasiński T, Smyth G, Maśko M. Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise.. Animals (Basel) 2022 Feb 12;12(4).
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