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Early and non-intrusive lameness detection in dairy cows using 3-dimensional video

Smith, Melvyn L.; Hansen, Mark F.; Smith, Lyndon N.; Abdul Jabbar, Khalid; Abdul Jabbar, K.; Hansen, Mark F.; Smith, Melvyn L.; Smith, Lyndon N.

Authors

Melvyn L. Smith

Mark F. Hansen

Lyndon N. Smith

Khalid Abdul Jabbar khalid.abduljabbar@uwe.ac.uk

K. Abdul Jabbar

Mark Hansen Mark.Hansen@uwe.ac.uk
Associate Professor in Knowledge Exchange & External Engagement

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Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof

Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine



Abstract

ABSTRACT
Lameness is a major issue in dairy herds and its early and automated detection offers animal welfare benefits together with high potential commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness. A novel proxy for lameness using 3-dimensional (3D) depth video data to analyse the animal’s gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint during walking. The video capture setup is completely covert and it facilitates an automated process. The animals are recorded using an overhead 3D depth camera as they walk freely in single file after the milking session. A 3D depth image of the cow’s body is used to automatically track key regions such as the hooks and the spine. The height movements are calculated from these regions to form the locomotion signals of this study, which are analysed using a Hilbert transform. Our results using a 1-5 locomotion scoring (LS) system on 22 Holstein Friesian dairy cows, a threshold could be identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome thereby minimising losses and reducing animal suffering. Using a linear Support Vector Machine (SVM) binary classification model, the threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows) and 75% specificity (detecting non-lame cows).

Citation

Abdul Jabbar, K., Hansen, M. F., Smith, M., & Smith, L. (2017). Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosystems Engineering, 153, 63-69. https://doi.org/10.1016/j.biosystemseng.2016.09.017

Journal Article Type Article
Acceptance Date Sep 12, 2016
Online Publication Date Nov 26, 2016
Publication Date Feb 1, 2017
Journal Biosystems Engineering
Print ISSN 1537-5110
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 153
Pages 63-69
DOI https://doi.org/10.1016/j.biosystemseng.2016.09.017
Keywords 3D computer vision, early lameness detection, gait asymmetry, locomotion analysis
Public URL https://uwe-repository.worktribe.com/output/898942
Publisher URL http://dx.doi.org/10.1016/j.biosystemseng.2016.09.017

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