Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Abdul Jabbar, K.; Hansen, M. F.; Smith, M.; Smith, L. (2017)
Publisher: Elsevier
Languages: English
Types: Article
ABSTRACT\ud 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).
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Breuer, K., Hemsworth, P., Barnett, J., Matthews, L., & Coleman, G. (2000). Behavioural response to humans and the productivity of commercial dairy cows. Applied Animal Behaviour Science, 66(4), 273-288.
    • Cha, E., Hertl, J., Bar, D., & Gröhn, Y. (2010). The cost of different types of lameness in dairy cows calculated by dynamic programming. Preventive Veterinary Medicine, 97(1), 1-8.
    • Chapinal, N., de Passillé, A., Rushen, J., & Wagner, S. (2010). Effect of analgesia during hoof trimming on gait, weight distribution, and activity of dairy cattle. Journal of Dairy Science, 93(7), 3039-3046.
    • de Mol, R., André, G., Bleumer, E., van der Werf, J., de Haas, Y., & van Reenen, C. (2013). Applicability of dayto-day variation in behavior for the automated detection of lameness in dairy cows. Journal of Dairy Science, 96(6), 3703-3712.
    • Flower, F., Sanderson, D., & Weary, D. (2006). Effects of Milking on Dairy Cow Gait. Journal of Dairy Science, 89(6), 2084-2089.
    • Grandin, T. (2010). Improving animal welfare. Wallingford, Oxfordshire, UK: CAB International.
    • Hildebrand, M., D.M. Bramble, K.F. Liem, and D.B. Wake. (1985). Functional vertebrate morphology. Cambridge, Mass.: Belknap Press of Harvard University Press.
    • Koenderink, J. & van Doorn, A. (1992). Surface shape and curvature scales. Image And Vision Computing, 10(8), 557-564.
    • Leach, K., Tisdall, D., Bell, N., Main, D., & Green, L. (2012). The effects of early treatment for hindlimb lameness in dairy cows on four commercial UK farms. The Veterinary Journal, 193(3), 626-632.
    • Maertens, W., Vangeyte, J., Baert, J., Jantuan, A., Mertens, K., & De Campeneere, S. et al. (2011). Development of a real time cow gait tracking and analysing tool to assess lameness using a pressure sensitive walkway: The GAITWISE system. Biosystems Engineering, 110(1), 29-39.
    • Neveux, S., Weary, D., Rushen, J., von Keyserlingk, M., & de Passillé, A. (2006). Hoof Discomfort Changes How Dairy Cattle Distribute Their Body Weight. Journal of Dairy Science, 89(7), 2503-2509.
    • Poursaberi, A., Bahr, C., Pluk, A., Van Nuffel, A., & Berckmans, D. (2010). Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques. Computers and Electronics in Agriculture, 74(1), 110-119.
    • Poursaberi, A., C. Bahr, A. Pluk, I. Veermae, E. Kokin, V. i. Pokalainen, and D. Berckmans. (2011). Online lameness detection in dairy cattle using body movement pattern (BMP). 732-736 in Proc. 11th International Conference Intelligent Systems Design and Applications (ISDA 2011).
    • Reader, J., Green, M., Kaler, J., Mason, S., & Green, L. (2011). Effect of mobility score on milk yield and activity in dairy cattle. Journal of Dairy Science, 94(10), 5045-5052.
    • Remy, C., Buffinton, K., & Siegwart, R. (2009). Stability Analysis of Passive Dynamic Walking of Quadrupeds. The International Journal of Robotics Research, 29(9), 1173-1185.
    • Sprecher, D., Hostetler, D., & Kaneene, J. (1997). A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology, 47(6), 1179-1187.
    • Thorup, V., do Nascimento, O., Skjøth, F., Voigt, M., Rasmussen, M., Bennedsgaard, T., & Ingvartsen, K. (2014). Short communication: Changes in gait symmetry in healthy and lame dairy cows based on 3- dimensional ground reaction force curves following claw trimming. Journal of Dairy Science, 97(12), 7679-7684.
    • van der Tol, P., Metz, J., Noordhuizen-Stassen, E., Back, W., Braam, C., & Weijs, W. (2005). Frictional Forces Required for Unrestrained Locomotion in Dairy Cattle. Journal of Dairy Science, 88(2), 615-624.
    • Van Hertem, T., Viazzi, S., Steensels, M., Maltz, E., Antler, A., & Alchanatis, V. et al. (2014). Automatic lameness detection based on consecutive 3D-video recordings. Biosystems Engineering, 119, 108-116.
    • Van Nuffel, A., Van De Gucht, T., Saeys, W., Sonck, B., Opsomer, G., & Vangeyte, J. et al. (2015). Environmental and cow-related factors affect cow locomotion and can cause misclassification in lameness detection systems. Animal, 10(09), 1533-1541.
    • Viazzi, S., Bahr, C., Van Hertem, T., Schlageter-Tello, A., Romanini, C., & Halachmi, I. et al. (2014). Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Computers and Electronics in Agriculture, 100, 139-147.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article