Sensor data classification for the indication of lameness in sheep

Research output: Contribution to Book/ReportConference Contribution

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 to determine 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.
Original languageEnglish
Title of host publicationCollaborative Computing: Networking, Applications and Worksharing
Subtitle of host publication13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings
EditorsIman Romdhani, Lei Shu, Hara Takahiro, Zhangbing Zhou, Timothy Gordon, Deze Zeng
Place of PublicationEdinburgh
PublisherSpringer International Publishing
Pages309-320
Number of pages12
Edition1
ISBN (Electronic)9783030009168
ISBN (Print)9783030009151
DOIs
Publication statusE-pub ahead of print - 26 Sep 2018
EventCollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing - Edinburgh, United Kingdom
Duration: 11 Dec 201713 Dec 2017
http://collaboratecom.eai-conferences.org/

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series
Volume252

Conference

ConferenceCollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing
CountryUnited Kingdom
CityEdinburgh
Period11/12/1713/12/17
Internet address

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Keywords

  • sensor data analysis
  • classification
  • sheep lameness detection
  • machine learning

Cite this

Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D., & Turner, S. J. (2018). Sensor data classification for the indication of lameness in sheep. In I. Romdhani, L. Shu, H. Takahiro, Z. Zhou, T. Gordon, & D. Zeng (Eds.), Collaborative Computing: Networking, Applications and Worksharing: 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings (1 ed., pp. 309-320). (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series; Vol. 252). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-00916-8_29