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Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equ...

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Autores principales: Domino, Małgorzata, Borowska, Marta, Zdrojkowski, Łukasz, Jasiński, Tomasz, Sikorska, Urszula, Skibniewski, Michał, Maśko, Małgorzata
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414866/
https://www.ncbi.nlm.nih.gov/pubmed/36015813
http://dx.doi.org/10.3390/s22166052
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author Domino, Małgorzata
Borowska, Marta
Zdrojkowski, Łukasz
Jasiński, Tomasz
Sikorska, Urszula
Skibniewski, Michał
Maśko, Małgorzata
author_facet Domino, Małgorzata
Borowska, Marta
Zdrojkowski, Łukasz
Jasiński, Tomasz
Sikorska, Urszula
Skibniewski, Michał
Maśko, Małgorzata
author_sort Domino, Małgorzata
collection PubMed
description As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.
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spelling pubmed-94148662022-08-27 Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding Domino, Małgorzata Borowska, Marta Zdrojkowski, Łukasz Jasiński, Tomasz Sikorska, Urszula Skibniewski, Michał Maśko, Małgorzata Sensors (Basel) Article As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features. MDPI 2022-08-13 /pmc/articles/PMC9414866/ /pubmed/36015813 http://dx.doi.org/10.3390/s22166052 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Domino, Małgorzata
Borowska, Marta
Zdrojkowski, Łukasz
Jasiński, Tomasz
Sikorska, Urszula
Skibniewski, Michał
Maśko, Małgorzata
Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
title Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
title_full Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
title_fullStr Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
title_full_unstemmed Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
title_short Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding
title_sort application of the two-dimensional entropy measures in the infrared thermography-based detection of rider: horse bodyweight ratio in horseback riding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414866/
https://www.ncbi.nlm.nih.gov/pubmed/36015813
http://dx.doi.org/10.3390/s22166052
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