Research Article
Sensor Data Classification for the Indication of Lameness in Sheep
@INPROCEEDINGS{10.1007/978-3-030-00916-8_29, author={Zainab Al-Rubaye and Ali Al-Sherbaz and Wanda McCormick and Scott Turner}, title={Sensor Data Classification for the Indication of Lameness in Sheep}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings}, proceedings_a={COLLABORATECOM}, year={2018}, month={10}, keywords={Sensor data classification Machine learning Decision tree Lameness detection Sheep}, doi={10.1007/978-3-030-00916-8_29} }- Zainab Al-Rubaye
Ali Al-Sherbaz
Wanda McCormick
Scott Turner
Year: 2018
Sensor Data Classification for the Indication of Lameness in Sheep
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_29
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.
