Equine Welfare Assessment: Horse Motion Evaluation and Comparison to Manual Pain Measurements

  • Dominik RueßEmail author
  • Jochen Rueß
  • Christian Hümmer
  • Niklas Deckers
  • Vitaliy Migal
  • Kathrin Kienapfel
  • Anne Wieckert
  • Dirk Barnewitz
  • Ralf Reulke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11854)


Pain estimation for horses is a tedious task since they are flight animals and tend to suppress pain specific behaviour. We obtained continuous video data of horse in different levels of pain, to evaluate their behaviour – while they were undergoing routine castration. During the whole time we regularly and manually evaluated the horses’ pain.

To quantify the horses’ motions, we automatically extracted horse masks from which we derive their orientation and position. We then performed a motion feature selection based on the different types of manual pain measurements. This is thus a first time comparison of long term manual and automatically derived equine pain evaluation.

A result is the decreased motion entropy of horses in pain and a tendency of staying in a place for longer periods of time. This was reflected with large observation time windows and features related to this behaviour – features which measure the entropy, for instance, or the change quantiles. It also turned out horses behave differently when humans are nearby, thus this method gives an unbiased view on the pain behaviour, especially motion under pain conditions.

These results can decrease the workload in veterinary clinics and provides means of remotely displaying potential discomfort of horses.


Image processing Signal processing Equine welfare monitoring Time series Pain estimation 


  1. 1.
    von Borell, E., et al.: Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals - a review. Physiol. Behav. 92(3), 293–316 (2007)CrossRefGoogle Scholar
  2. 2.
    Bussières, G., et al.: Development of a composite orthopaedic pain scale in horses. Res. Vet. Sci. 85(2), 294–306 (2008)CrossRefGoogle Scholar
  3. 3.
    Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv (2017)Google Scholar
  4. 4.
    Costa, A., Ismayilova, G., Borgonovo, F., Viazzi, S., Berckmans, D., Guarino, M.: Image-processing technique to measure pig activity in response to climatic variation in a pig barn. Anim. Prod. Sci. 54(8), 1075–1083 (2014)CrossRefGoogle Scholar
  5. 5.
    Dalla Costa, E., Minero, M., Lebelt, D., Stucke, D., Canali, E., Leach, M.C.: Development of the Horse Grimace Scale (HGS) as a pain assessment tool in horses undergoing routine castration. PLoS ONE 9(3), e92281 (2014)CrossRefGoogle Scholar
  6. 6.
    Guzhva, O., Ardö, H., Nilsson, M., Herlin, A., Tufvesson, L.: Now you see me: convolutional neural network based tracker for dairy cows. Front. Robot. AI 5, 107 (2018)CrossRefGoogle Scholar
  7. 7.
    van Loon, J.P., Back, W., Hellebrekers, L.J., van Weeren, P.R.: Application of a composite pain scale to objectively monitor horses with somatic and visceral pain under hospital conditions. J. Equine Vet. Sci. 30(11), 641–649 (2010)CrossRefGoogle Scholar
  8. 8.
    Matthews, S.G., Miller, A.L., Clapp, J., Plötz, T., Kyriazakis, I.: Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. Vet. J. 217, 43–51 (2016)CrossRefGoogle Scholar
  9. 9.
    Merskey, H.: Pain terms: a list with definitions and notes on usage. Recommended IASP Subcommittee on Taxonomy. Pain 6(3), 249 (1979)Google Scholar
  10. 10.
    Nasirahmadi, A.: Development of automated computer vision systems for investigation of livestock behaviours (2017)Google Scholar
  11. 11.
    Nilsson, M., Herlin, A.H., Ardö, H., Guzhva, O., Åström, K., Bergsten, C.: Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal 9(11), 1859–1865 (2015)CrossRefGoogle Scholar
  12. 12.
    Pezzuolo, A., Guarino, M., Sartori, L., González, L.A., Marinello, F.: On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera. Comput. Electron. Agric. 148, 29–36 (2018)CrossRefGoogle Scholar
  13. 13.
    Price, J., Catriona, S., Welsh, E.M., Waran, N.K.: Preliminary evaluation of a behaviour-based system for assessment of post-operative pain in horses following arthroscopic surgery. Veterinary Anaesth. Analg. 30(3), 124–137 (2003)CrossRefGoogle Scholar
  14. 14.
    Reulke, R., Rueß, D., Deckers, N., Barnewitz, D., Wieckert, A., Kienapfel, K: Analysis of motion patterns for pain estimation of horses. In: 15th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1–6 (2018)Google Scholar
  15. 15.
    Shao, B., Xin, H.: A real-time computer vision assessment and control of thermal comfort for group-housed pigs. Comput. Electron. Agric. 62(1), 15–21 (2008)CrossRefGoogle Scholar
  16. 16.
    Wagner, A.E.: Effects of stress on pain in horses and incorporating pain scales for equine practice. Vet. Clin. Equine Pract. 26(3), 481–492 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Humboldt-Universität zu Berlin, Institut für InformatikBerlinGermany
  2. 2.Ruhr-Universität BochumBochumGermany
  3. 3.Tierärztliche Klinik für PferdeDemminGermany
  4. 4.Tierärztliche Klinik der fzmb GmbHBad LangensalzaGermany

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