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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9187730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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