Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations.
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | https://www.repositorio.ufop.br/handle/123456789/18754 https://doi.org/10.3390/ani14071109 |
Resumo: | Although direct behavioural observations are widely used, they are time-consuming, prone to error, require knowledge of the observed species, and depend on intra/inter-observer consistency. As a result, they pose challenges to the reliability and repeatability of studies. Automated video analysis is becoming popular for behavioural observations. Sleep is a biological metric that has the potential to become a reliable broad-spectrum metric that can indicate the quality of life and understanding sleep patterns can contribute to identifying and addressing potential welfare concerns, such as stress, discomfort, or health issues, thus promoting the overall welfare of animals; however, due to the laborious process of quantifying sleep patterns, it has been overlooked in animal welfare research. This study presents a system comparing convolutional neural networks (CNNs) with direct behavioural observation methods for the same data to detect and quantify dogs’ sleeping patterns. A total of 13,688 videos were used to develop and train the model to quantify sleep duration and sleep fragmentation in dogs. To evaluate its similarity to the direct behavioural observations made by a single human observer, 6000 previously unseen frames were used. The system successfully classified 5430 frames, scoring a similarity rate of 89% when compared to the manually recorded observations. There was no significant difference in the percentage of time observed between the system and the human observer (p > 0.05). However, a significant difference was found in total sleep time recorded, where the automated system captured more hours than the observer (p < 0.05). This highlights the potential of using a CNN-based system to study animal welfare and behaviour research. |
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Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations.Animal welfareBehavioural observationsComputer visionAlthough direct behavioural observations are widely used, they are time-consuming, prone to error, require knowledge of the observed species, and depend on intra/inter-observer consistency. As a result, they pose challenges to the reliability and repeatability of studies. Automated video analysis is becoming popular for behavioural observations. Sleep is a biological metric that has the potential to become a reliable broad-spectrum metric that can indicate the quality of life and understanding sleep patterns can contribute to identifying and addressing potential welfare concerns, such as stress, discomfort, or health issues, thus promoting the overall welfare of animals; however, due to the laborious process of quantifying sleep patterns, it has been overlooked in animal welfare research. This study presents a system comparing convolutional neural networks (CNNs) with direct behavioural observation methods for the same data to detect and quantify dogs’ sleeping patterns. A total of 13,688 videos were used to develop and train the model to quantify sleep duration and sleep fragmentation in dogs. To evaluate its similarity to the direct behavioural observations made by a single human observer, 6000 previously unseen frames were used. The system successfully classified 5430 frames, scoring a similarity rate of 89% when compared to the manually recorded observations. There was no significant difference in the percentage of time observed between the system and the human observer (p > 0.05). However, a significant difference was found in total sleep time recorded, where the automated system captured more hours than the observer (p < 0.05). This highlights the potential of using a CNN-based system to study animal welfare and behaviour research.2024-10-03T21:28:03Z2024-10-03T21:28:03Z2024info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSCHORK, I. G. et al. Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. Animals, v. 14, artigo 1109, 2024. Disponível em: <https://www.mdpi.com/2076-2615/14/7/1109>. Acesso em: 15 ago. 2024.2076-2615https://www.repositorio.ufop.br/handle/123456789/18754https://doi.org/10.3390/ani14071109This 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/). Fonte: PDF do artigo.info:eu-repo/semantics/openAccessSchork, Ivana GabrielaZamansky, AnnaFarhat, NareedAzevedo, Cristiano Schetini deYoung, Robert Johnengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOP2024-10-03T21:28:08Zoai:repositorio.ufop.br:123456789/18754Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332024-10-03T21:28:08Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.none.fl_str_mv |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
title |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
spellingShingle |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. Schork, Ivana Gabriela Animal welfare Behavioural observations Computer vision |
title_short |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
title_full |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
title_fullStr |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
title_full_unstemmed |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
title_sort |
Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. |
author |
Schork, Ivana Gabriela |
author_facet |
Schork, Ivana Gabriela Zamansky, Anna Farhat, Nareed Azevedo, Cristiano Schetini de Young, Robert John |
author_role |
author |
author2 |
Zamansky, Anna Farhat, Nareed Azevedo, Cristiano Schetini de Young, Robert John |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Schork, Ivana Gabriela Zamansky, Anna Farhat, Nareed Azevedo, Cristiano Schetini de Young, Robert John |
dc.subject.por.fl_str_mv |
Animal welfare Behavioural observations Computer vision |
topic |
Animal welfare Behavioural observations Computer vision |
description |
Although direct behavioural observations are widely used, they are time-consuming, prone to error, require knowledge of the observed species, and depend on intra/inter-observer consistency. As a result, they pose challenges to the reliability and repeatability of studies. Automated video analysis is becoming popular for behavioural observations. Sleep is a biological metric that has the potential to become a reliable broad-spectrum metric that can indicate the quality of life and understanding sleep patterns can contribute to identifying and addressing potential welfare concerns, such as stress, discomfort, or health issues, thus promoting the overall welfare of animals; however, due to the laborious process of quantifying sleep patterns, it has been overlooked in animal welfare research. This study presents a system comparing convolutional neural networks (CNNs) with direct behavioural observation methods for the same data to detect and quantify dogs’ sleeping patterns. A total of 13,688 videos were used to develop and train the model to quantify sleep duration and sleep fragmentation in dogs. To evaluate its similarity to the direct behavioural observations made by a single human observer, 6000 previously unseen frames were used. The system successfully classified 5430 frames, scoring a similarity rate of 89% when compared to the manually recorded observations. There was no significant difference in the percentage of time observed between the system and the human observer (p > 0.05). However, a significant difference was found in total sleep time recorded, where the automated system captured more hours than the observer (p < 0.05). This highlights the potential of using a CNN-based system to study animal welfare and behaviour research. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-10-03T21:28:03Z 2024-10-03T21:28:03Z 2024 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
SCHORK, I. G. et al. Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. Animals, v. 14, artigo 1109, 2024. Disponível em: <https://www.mdpi.com/2076-2615/14/7/1109>. Acesso em: 15 ago. 2024. 2076-2615 https://www.repositorio.ufop.br/handle/123456789/18754 https://doi.org/10.3390/ani14071109 |
identifier_str_mv |
SCHORK, I. G. et al. Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations. Animals, v. 14, artigo 1109, 2024. Disponível em: <https://www.mdpi.com/2076-2615/14/7/1109>. Acesso em: 15 ago. 2024. 2076-2615 |
url |
https://www.repositorio.ufop.br/handle/123456789/18754 https://doi.org/10.3390/ani14071109 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFOP instname:Universidade Federal de Ouro Preto (UFOP) instacron:UFOP |
instname_str |
Universidade Federal de Ouro Preto (UFOP) |
instacron_str |
UFOP |
institution |
UFOP |
reponame_str |
Repositório Institucional da UFOP |
collection |
Repositório Institucional da UFOP |
repository.name.fl_str_mv |
Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP) |
repository.mail.fl_str_mv |
repositorio@ufop.edu.br |
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1813002828540542976 |