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Data Science Approaches for the Analysis of Animal Behaviours

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Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

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Abstract

Animals’ welfare can be categorized and predicted by observing their daily routine and behaviour and consequently, conclusions about their health status and their ecology can be made. Yet, observing the animals’ activity is time-consuming and labour intensive, therefore there is a need to develop automated behavioural monitoring systems for more efficient and effective computerised agriculture. In this study, accelerometer and gyroscope measurements were collected from seven Hebridean ewes located in Cheshire, UK. Once the activities of the animals were labelled as grazing, resting, walking, browsing, scratching, and standing, data analysis was conducted. The performance of the random forest was evaluated as we have previously suggested that this algorithm can provide advantages and has been proven to adequately classify the behaviours of the animals. When using features from both the accelerometer and gyroscope, the algorithm obtained the best results having accuracy and kappa value of 96.43%, and 95.02%, respectively. However, using data from the accelerometer exclusively, only decreased the accuracy by 0.40% and kappa value by 0.56%. Therefore, in future work, we will consider only the use of accelerometer sensor and we will test the performance of the same features and random forest for real-time activity recognition using larger cohort of animals as well as mixed breed animals.

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References

  1. McLennan, K.M., et al.: Technical note: validation of an automatic recording system to assess behavioural activity level in sheep (Ovis aries). Small Rumin. Res. 127, 92–96 (2015)

    Article  Google Scholar 

  2. Gougoulis, D.A., Kyriazakis, I., Fthenakis, G.C.: Diagnostic significance of behaviour changes of sheep: a selected review. Small Rumin. Res. 92, 52–56 (2010)

    Article  Google Scholar 

  3. Barwick, J., Lamb, D., Dobos, R., Schneider, D., Welch, M., Trotter, M.: Predicting lameness in sheep activity using tri-axial acceleration signals. Animals 8, 12 (2018)

    Article  Google Scholar 

  4. Rutter, S.M.: Advanced livestock management solutions. In: Ferguson, D.M., Lee, C., Fisher, A. (eds.) Advances in Sheep Welfare, pp. 245–261. Woodhead Publishing, Sawston (2017)

    Chapter  Google Scholar 

  5. Frost, A., Schofield, C., Beaulah, S., Mottram, T., Lines, J., Wathes, C.: A review of livestock monitoring and the needs for integrated systems. Comput. Electron. Agric. 17, 139–159 (1997)

    Article  Google Scholar 

  6. Anderson, D.M., Estell, R.E., Holechek, J.L., Ivey, S., Smith, G.B.: Virtual herding for flexible livestock management - a review. Rangeland J. 36, 205–221 (2014)

    Article  Google Scholar 

  7. Norton, B.E., Barnes, M., Teague, R.: Grazing management can improve livestock distribution: increasing accessible forage and effective grazing capacity. Rangelands 35, 45–51 (2013)

    Article  Google Scholar 

  8. Shepard, E.L.C., et al.: Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2010)

    Article  Google Scholar 

  9. Kleanthous, N., et al.: Machine learning techniques for classification of livestock behavior. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11304, pp. 304–315. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04212-7_26

    Chapter  Google Scholar 

  10. Hounslow, J.L., et al.: Assessing the effects of sampling frequency on behavioural classification of accelerometer data. J. Exp. Mar. Bio. Ecol. 512, 22–30 (2019)

    Article  Google Scholar 

  11. Walton, E., et al.: Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour. R. Soc. Open Sci. 5, 171442 (2018)

    Article  Google Scholar 

  12. Krahnstoever, N., Rittscher, J., Tu, P., Chean, K., Tomlinson, T.: Activity recognition using visual tracking and RFID. In: 2005 Seventh IEEE Workshops on Application of Computer Vision. WACV/MOTIONS 2005, vol. 1, pp. 494–500 (2005)

    Google Scholar 

  13. Cangar, Ö., et al.: Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis. Comput. Electron. Agric. 64, 53–60 (2008)

    Article  Google Scholar 

  14. Schlecht, E., Hülsebusch, C., Mahler, F., Becker, K.: The use of differentially corrected global positioning system to monitor activities of cattle at pasture. Appl. Anim. Behav. Sci. 85, 185–202 (2004)

    Article  Google Scholar 

  15. Ungar, E.D., Henkin, Z., Gutman, M., Dolev, A., Genizi, A., Ganskopp, D.: Inference of animal activity from GPS collar data on free-ranging cattle. Rangeland Ecol. Manag. 58, 256–266 (2005)

    Article  Google Scholar 

  16. Schwager, M., Anderson, D.M., Butler, Z., Rus, D.: Robust classification of animal tracking data. Comput. Electron. Agric. 56, 46–59 (2007)

    Article  Google Scholar 

  17. Umstätter, C., Waterhouse, A., Holland, J.P.: An automated sensor-based method of simple behavioural classification of sheep in extensive systems. Comput. Electron. Agric. 64, 19–26 (2008)

    Article  Google Scholar 

  18. Brewster, L.R., et al.: Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data. Mar. Biol. 165, 62 (2018)

    Article  Google Scholar 

  19. Kamminga, J.W., Bisby, H.C., Le, D.V., Meratnia, N., Havinga, P.J.M.: Generic online animal activity recognition on collar tags. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers on – UbiComp 2017, pp. 597–606. ACM, New York (2017)

    Google Scholar 

  20. Mason, A., Sneddon, J.: Automated monitoring of foraging behaviour in free ranging sheep grazing a biodiverse pasture. In: 2013 Seventh International Conference on Sensing Technology (ICST), pp. 46–51 (2013)

    Google Scholar 

  21. Radeski, M., Ilieski, V.: Gait and posture discrimination in sheep using a tri-axial accelerometer. Animal 11, 1249–1257 (2017)

    Article  Google Scholar 

  22. Le Roux, S., et al.: An overview of automatic behaviour classification for animal-borne sensor applications in South Africa (2017)

    Google Scholar 

  23. Werner, J., et al.: Evaluation and application potential of an accelerometer-based collar device for measuring grazing behavior of dairy cows. In: Animal, pp. 1–10 (2019)

    Google Scholar 

  24. Moreau, M., Siebert, S., Buerkert, A., Schlecht, E.: Use of a tri-axial accelerometer for automated recording and classification of goats’ grazing behaviour. Appl. Anim. Behav. Sci. 119, 158–170 (2009)

    Article  Google Scholar 

  25. Hu, W., Jia, C.: A bootstrapping approach to entity linkage on the Semantic Web. Web Semant. Sci. Serv. Agents World Wide Web 34, 1–12 (2015)

    Article  Google Scholar 

  26. Ladds, M.A., et al.: Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry. Animal Biotelemetry 5, 8 (2017)

    Article  Google Scholar 

  27. González, L.A.A., Bishop-Hurley, G.J.J., Handcock, R.N.N., Crossman, C.: Behavioral classification of data from collars containing motion sensors in grazing cattle. Comput. Electron. Agric. 110, 91–102 (2015)

    Article  Google Scholar 

  28. Gutierrez-Galan, D., et al.: Embedded neural network for real-time animal behavior classification. Neurocomputing 272, 17–26 (2018)

    Article  Google Scholar 

  29. Arcidiacono, C., Porto, S.M.C.C., Mancino, M., Cascone, G.: Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Comput. Electron. Agric. 134, 124–134 (2017)

    Article  Google Scholar 

  30. Nadimi, E.S., Jørgensen, R.N., Blanes-Vidal, V., Christensen, S.: Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks. Comput. Electron. Agric. 82, 44–54 (2012)

    Article  Google Scholar 

  31. Marais, J., et al.: Automatic classification of sheep behaviour using 3-axis accelerometer data (2014)

    Google Scholar 

  32. Alvarenga, F.A.P., Borges, I., Palkovič, L., Rodina, J., Oddy, V.H., Dobos, R.C.: Using a three-axis accelerometer to identify and classify sheep behaviour at pasture. Appl. Anim. Behav. Sci. 181, 91–99 (2016)

    Article  Google Scholar 

  33. le Roux, S.P., Marias, J., Wolhuter, R., Niesler, T.: Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis). Animal Biotelemetry 5, 25 (2017)

    Article  Google Scholar 

  34. Giovanetti, V., et al.: Automatic classification system for grazing, ruminating and resting behaviour of dairy sheep using a tri-axial accelerometer. Livest. Sci. 196, 42–48 (2017)

    Article  Google Scholar 

  35. Barwick, J., Lamb, D.W., Dobos, R., Welch, M., Trotter, M.: Categorising sheep activity using a tri-axial accelerometer. Comput. Electron. Agric. 145, 289–297 (2018)

    Article  Google Scholar 

  36. Decandia, M., et al.: The effect of different time epoch settings on the classification of sheep behaviour using tri-axial accelerometry. Comput. Electron. Agric. 154, 112–119 (2018)

    Article  Google Scholar 

  37. Kamminga, J.W., Le, D.V., Meijers, J.P., Bisby, H., Meratnia, N., Havinga, P.J.M.: Robust sensor-orientation-independent feature selection for animal activity recognition on collar tags. In: Proceedings of ACM Interactive, Mobile, Wearable Ubiquitous Technology, vol. 2, pp. 1–27 (2018)

    Article  Google Scholar 

  38. Mansbridge, N., et al.: Feature selection and comparison of machine learning algorithms in classification of grazing and rumination behaviour in sheep. Sensors (Switzerland) 18, 1–16 (2018)

    Article  Google Scholar 

  39. le Roux, S.P., Wolhuter, R., Niesler, T.: Energy-aware feature and model selection for onboard behavior classification in low-power animal borne sensor applications. IEEE Sens. J. 19, 2722–2734 (2019)

    Article  Google Scholar 

  40. HyperIMU – ianovir.com. https://ianovir.com/works/mobile/hyperimu/

  41. Yang, X., Dinh, A., Chen, L.: Implementation of a wearerable real-time system for physical activity recognition based on Naive Bayes classifier. In: 2010 International Conference on Bioinformatics and Biomedical Technology, pp. 101–105. IEEE (2010)

    Google Scholar 

  42. Bouten, C.V., Westerterp, K.R., Verduin, M., Janssen, J.D.: Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med. Sci. Sports Exerc. 26, 1516–1523 (1994)

    Article  Google Scholar 

  43. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  44. Rifkin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5, 101–141 (2004)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This study is funded by The Douglas Bomford Trust and John Moores University.

Author information

Authors and Affiliations

  1. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Natasa Kleanthous & Abir Hussain

  2. Animalia AS, Norwegian Meat and Poultry Research Institute, Oslo, Norway

    Alex Mason

  3. Faculty of Science and Technology, Norwegian University of Life Sciences, 1432, Ås, Norway

    Alex Mason

  4. Department of Built Environment, Liverpool John Moores University, Liverpool, UK

    Alex Mason

  5. Department of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK

    Jennifer Sneddon

Authors
  1. Natasa Kleanthous
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  2. Abir Hussain
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  3. Alex Mason
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  4. Jennifer Sneddon
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Corresponding authors

Correspondence to Natasa Kleanthous or Abir Hussain .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

  3. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Kleanthous, N., Hussain, A., Mason, A., Sneddon, J. (2019). Data Science Approaches for the Analysis of Animal Behaviours. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-26766-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

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