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Automated recognition of pain in cats

Facial expressions in non-human animals are closely linked to their internal affective states, with the majority of empirical work focusing on facial shape changes associated with pain. However, existing tools for facial expression analysis are prone to human subjectivity and bias, and in many cases...

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Autores principales: Feighelstein, Marcelo, Shimshoni, Ilan, Finka, Lauren R., Luna, Stelio P. L., Mills, Daniel S., Zamansky, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187730/
https://www.ncbi.nlm.nih.gov/pubmed/35688852
http://dx.doi.org/10.1038/s41598-022-13348-1
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author Feighelstein, Marcelo
Shimshoni, Ilan
Finka, Lauren R.
Luna, Stelio P. L.
Mills, Daniel S.
Zamansky, Anna
author_facet Feighelstein, Marcelo
Shimshoni, Ilan
Finka, Lauren R.
Luna, Stelio P. L.
Mills, Daniel S.
Zamansky, Anna
author_sort Feighelstein, Marcelo
collection PubMed
description Facial expressions in non-human animals are closely linked to their internal affective states, with the majority of empirical work focusing on facial shape changes associated with pain. However, existing tools for facial expression analysis are prone to human subjectivity and bias, and in many cases also require special expertise and training. This paper presents the first comparative study of two different paths towards automatizing pain recognition in facial images of domestic short haired cats (n = 29), captured during ovariohysterectomy at different time points corresponding to varying intensities of pain. One approach is based on convolutional neural networks (ResNet50), while the other—on machine learning models based on geometric landmarks analysis inspired by species specific Facial Action Coding Systems (i.e. catFACS). Both types of approaches reach comparable accuracy of above 72%, indicating their potential usefulness as a basis for automating cat pain detection from images.
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spelling pubmed-91877302022-06-12 Automated recognition of pain in cats Feighelstein, Marcelo Shimshoni, Ilan Finka, Lauren R. Luna, Stelio P. L. Mills, Daniel S. Zamansky, Anna Sci Rep Article Facial expressions in non-human animals are closely linked to their internal affective states, with the majority of empirical work focusing on facial shape changes associated with pain. However, existing tools for facial expression analysis are prone to human subjectivity and bias, and in many cases also require special expertise and training. This paper presents the first comparative study of two different paths towards automatizing pain recognition in facial images of domestic short haired cats (n = 29), captured during ovariohysterectomy at different time points corresponding to varying intensities of pain. One approach is based on convolutional neural networks (ResNet50), while the other—on machine learning models based on geometric landmarks analysis inspired by species specific Facial Action Coding Systems (i.e. catFACS). Both types of approaches reach comparable accuracy of above 72%, indicating their potential usefulness as a basis for automating cat pain detection from images. Nature Publishing Group UK 2022-06-10 /pmc/articles/PMC9187730/ /pubmed/35688852 http://dx.doi.org/10.1038/s41598-022-13348-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Feighelstein, Marcelo
Shimshoni, Ilan
Finka, Lauren R.
Luna, Stelio P. L.
Mills, Daniel S.
Zamansky, Anna
Automated recognition of pain in cats
title Automated recognition of pain in cats
title_full Automated recognition of pain in cats
title_fullStr Automated recognition of pain in cats
title_full_unstemmed Automated recognition of pain in cats
title_short Automated recognition of pain in cats
title_sort automated recognition of pain in cats
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187730/
https://www.ncbi.nlm.nih.gov/pubmed/35688852
http://dx.doi.org/10.1038/s41598-022-13348-1
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