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Sensor Data Classification for the Indication of Lameness in Sheep

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2017)

Abstract

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed at determining the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep.

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Acknowledgement

With great appreciation to the Ministry of Higher Education and Scientific Research in Iraq for the financial support. Many thanks to the University of Northampton and Moulton College for the cooperative efforts to provide a satisfactory academic environment. Many thanks for the Lodge Farm Shepherd ‘Tim’ who was very helpful in scheduling the observation time at Lodge Farm.

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Authors and Affiliations

  1. Department of Computing and Immersive Technologies, School of Art, Science and Technology, Northampton, NN2 6JD, UK

    Zainab Al-Rubaye, Ali Al-Sherbaz & Scott Turner

  2. Department of Biology, Faculty of Science and Technology, Anglia Ruskin University, Cambridge, CB1 1PT, UK

    Wanda McCormick

  3. Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq

    Zainab Al-Rubaye

Authors
  1. Zainab Al-Rubaye
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  2. Ali Al-Sherbaz
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  3. Wanda McCormick
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  4. Scott Turner
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Corresponding author

Correspondence to Zainab Al-Rubaye .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Imed Romdhani

  2. Guangdong University of Petrochemical Technology, Maoming, China

    Lei Shu

  3. Osaka University, Osaka, Japan

    Hara Takahiro

  4. China University of Geosciences, Beijing, China

    Zhangbing Zhou

  5. University of Nebraska–Lincoln, Lincoln, UK

    Timothy Gordon

  6. School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

    Deze Zeng

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W., Turner, S. (2018). Sensor Data Classification for the Indication of Lameness in Sheep. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_29

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  • DOI: https://doi.org/10.1007/978-3-030-00916-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00915-1

  • Online ISBN: 978-3-030-00916-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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