Automated observations of dogs’ resting behaviour patterns using artificial intelligence and their similarity to behavioural observations.

Detalhes bibliográficos
Autor(a) principal: Schork, Ivana Gabriela
Data de Publicação: 2024
Outros Autores: Zamansky, Anna, Farhat, Nareed, Azevedo, Cristiano Schetini de, Young, Robert John
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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spelling 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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