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3D pose estimation to detect posture transition in free-stall housed dairy cows
Swedish University of Agricultural sciences, Uppsala.
Dalarna University, School of Information and Engineering, Statistics.ORCID iD: 0000-0002-3183-3756
Swedish University of Agricultural sciences, Uppsala.
Sony Nordic, Lund.
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2024 (English)In: Journal of Dairy Science, ISSN 0022-0302, E-ISSN 1525-3198Article in journal (Refereed) Epub ahead of print
Abstract [en]

Free stall comfort is reflected in various indicators, including the ability for dairy cattle to display unhindered posture transition movements in the cubicles. To ensure farm animal welfare, it is instrumental for the farm management to be able to continuously monitor occurrences of abnormal motions. Advances in computer vision have enabled accurate kinematic measurements in several fields such as human, equine and bovine biomechanics. An important step upstream to measuring displacement during posture transitions is to determine that the behavior is accurately detected. In this study, we propose a framework for detecting lying to standing posture transitions from 3D pose estimation data. A multi-view computer vision system recorded posture transitions between Dec. 2021 and Apr. 2022 in a Swedish stall housing 183 individual cows. The output data consisted of the 3D coordinates of specific anatomical landmarks. Sensitivity of posture transition detection was 88.2% while precision reached 99.5%. Analyzing those transition movements, breakpoints detected the timestamp of onset of the rising motion, which was compared with that annotated by observers. Agreement between observers, measured by intra-class correlation, was 0.85 between 3 human observers and 0.81 when adding the automated detection. The intra-observer mean absolute difference in annotated timestamps ranged from 0.4s to 0.7s. The mean absolute difference between each observer and the automated detection ranged from 1.0s to 1.3s. There was a significant difference in annotated timestamp between all observer pairs but not between the observers and the automated detection, leading to the conclusion that the automated detection does not introduce a distinct bias. We conclude that the model is able to accurately detect the phenomenon of interest and that it is equatable to an observer.

Place, publisher, year, edition, pages
2024.
National Category
Animal and Dairy Science
Identifiers
URN: urn:nbn:se:du-48393DOI: 10.3168/jds.2023-24427PubMedID: 38642651OAI: oai:DiVA.org:du-48393DiVA, id: diva2:1853704
Available from: 2024-04-23 Created: 2024-04-23 Last updated: 2024-04-23Bibliographically approved

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CiteExportLink to record
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  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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