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Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography
The impact of a rider’s and saddle’s mass on saddle thermal pattern distribution was evaluated using infrared thermography (IRT). Eighteen racehorses were ridden by four riders with their own saddle. Images of the saddle panels were captured at each of six thermographic examinations. On each image,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6709906/ https://www.ncbi.nlm.nih.gov/pubmed/31449556 http://dx.doi.org/10.1371/journal.pone.0221622 |
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author | Soroko, Maria Zaborski, Daniel Dudek, Krzysztof Yarnell, Kelly Górniak, Wanda Vardasca, Ricardo |
author_facet | Soroko, Maria Zaborski, Daniel Dudek, Krzysztof Yarnell, Kelly Górniak, Wanda Vardasca, Ricardo |
author_sort | Soroko, Maria |
collection | PubMed |
description | The impact of a rider’s and saddle’s mass on saddle thermal pattern distribution was evaluated using infrared thermography (IRT). Eighteen racehorses were ridden by four riders with their own saddle. Images of the saddle panels were captured at each of six thermographic examinations. On each image, six regions of interest (ROIs) were marked on the saddle panels. The mean temperature for each ROI was extracted. To evaluate the influence of load on saddle fit, 4 indicators were used: ΔT(max) (difference between the mean temperature of the warmest and coolest ROI); standard deviation of the mean temperature of the six ROIs; right/left; bridging/rocking and front/back thermal pattern indicator. Incorrect saddle fit was found in 25 measurements (23.1%) with ΔT(max) greater than 2°C. The relationships between rider and saddle fit as well as saddle fit and horse were significant (p<0.001). An average ΔT(max) in rider A was significantly higher than in other riders (p<0.001). The right/left thermal pattern differed significantly from the optimal value for riders A and B; while the bridging/rocking thermal pattern differed significantly from this value for riders A, C and D (p<0.05). Front saddle thermal pattern was most frequent for rider A (41.5%), whereas back saddle thermal pattern was most frequent for rider C (85.7%). Measurement of the mean temperature in 6 ROIs on saddle panels after training was helpful in assessing the influence of rider and saddle mass on saddle fit. IRT offered a non-invasive, rapid and simple method for assessing load on thermal pattern distribution in race saddles. |
format | Online Article Text |
id | pubmed-6709906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67099062019-09-10 Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography Soroko, Maria Zaborski, Daniel Dudek, Krzysztof Yarnell, Kelly Górniak, Wanda Vardasca, Ricardo PLoS One Research Article The impact of a rider’s and saddle’s mass on saddle thermal pattern distribution was evaluated using infrared thermography (IRT). Eighteen racehorses were ridden by four riders with their own saddle. Images of the saddle panels were captured at each of six thermographic examinations. On each image, six regions of interest (ROIs) were marked on the saddle panels. The mean temperature for each ROI was extracted. To evaluate the influence of load on saddle fit, 4 indicators were used: ΔT(max) (difference between the mean temperature of the warmest and coolest ROI); standard deviation of the mean temperature of the six ROIs; right/left; bridging/rocking and front/back thermal pattern indicator. Incorrect saddle fit was found in 25 measurements (23.1%) with ΔT(max) greater than 2°C. The relationships between rider and saddle fit as well as saddle fit and horse were significant (p<0.001). An average ΔT(max) in rider A was significantly higher than in other riders (p<0.001). The right/left thermal pattern differed significantly from the optimal value for riders A and B; while the bridging/rocking thermal pattern differed significantly from this value for riders A, C and D (p<0.05). Front saddle thermal pattern was most frequent for rider A (41.5%), whereas back saddle thermal pattern was most frequent for rider C (85.7%). Measurement of the mean temperature in 6 ROIs on saddle panels after training was helpful in assessing the influence of rider and saddle mass on saddle fit. IRT offered a non-invasive, rapid and simple method for assessing load on thermal pattern distribution in race saddles. Public Library of Science 2019-08-26 /pmc/articles/PMC6709906/ /pubmed/31449556 http://dx.doi.org/10.1371/journal.pone.0221622 Text en © 2019 Soroko et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Soroko, Maria Zaborski, Daniel Dudek, Krzysztof Yarnell, Kelly Górniak, Wanda Vardasca, Ricardo Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
title | Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
title_full | Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
title_fullStr | Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
title_full_unstemmed | Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
title_short | Evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
title_sort | evaluation of thermal pattern distributions in racehorse saddles using infrared thermography |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6709906/ https://www.ncbi.nlm.nih.gov/pubmed/31449556 http://dx.doi.org/10.1371/journal.pone.0221622 |
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