Sensor data classification for the indication of lameness in sheep

Zainab Al-Rubaye, Ali Al-Sherbaz, Wanda D McCormick, Scott J Turner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


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
EditorsIman Romdhani, Lei Shu, Hara Takahiro, Zhangbing Zhou, Timothy Gordon, Deze Zeng
PublisherSpringer International Publishing AG
Number of pages12
ISBN (Print)9783030009151
Publication statusPublished - 26 Sept 2018
Externally publishedYes

Publication series

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


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


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