Automated recognition of pain in cats
Abstract 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 m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-13348-1 |
| _version_ | 1818253061737414656 |
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| author | Marcelo Feighelstein Ilan Shimshoni Lauren R. Finka Stelio P. L. Luna Daniel S. Mills Anna Zamansky |
| author_facet | Marcelo Feighelstein Ilan Shimshoni Lauren R. Finka Stelio P. L. Luna Daniel S. Mills Anna Zamansky |
| author_sort | Marcelo Feighelstein |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T16:34:05Z |
| format | Article |
| id | doaj.art-07df435327fa4b989dbecfc4e88ea856 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T16:34:05Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-07df435327fa4b989dbecfc4e88ea8562022-12-22T00:18:41ZengNature PortfolioScientific Reports2045-23222022-06-0112111010.1038/s41598-022-13348-1Automated recognition of pain in catsMarcelo Feighelstein0Ilan Shimshoni1Lauren R. Finka2Stelio P. L. Luna3Daniel S. Mills4Anna Zamansky5Information Systems Department, University of HaifaInformation Systems Department, University of HaifaSchool of Veterinary Medicine and Science, The University of NottinghamDepartment of Veterinary Surgery and Animal Reproduction, School of Veterinary Medicine and Animal Science, São Paulo State University (Unesp)School of Life Sciences, Joseph Bank Laboratories, University of LincolnInformation Systems Department, University of HaifaAbstract 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.https://doi.org/10.1038/s41598-022-13348-1 |
| spellingShingle | Marcelo Feighelstein Ilan Shimshoni Lauren R. Finka Stelio P. L. Luna Daniel S. Mills Anna Zamansky Automated recognition of pain in cats Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-13348-1 |
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