Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Bussières, G., et al.: Development of a composite orthopaedic pain scale in horses. Res. Vet. Sci. 85(2), 294–306 (2008)
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv (2017)
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)
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)
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)
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)
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)
Merskey, H.: Pain terms: a list with definitions and notes on usage. Recommended IASP Subcommittee on Taxonomy. Pain 6(3), 249 (1979)
Nasirahmadi, A.: Development of automated computer vision systems for investigation of livestock behaviours (2017)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rueß, D. et al. (2019). Equine Welfare Assessment: Horse Motion Evaluation and Comparison to Manual Pain Measurements. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-34879-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34878-6
Online ISBN: 978-3-030-34879-3
eBook Packages: Computer ScienceComputer Science (R0)