Zainab_13th EAI _Final version.docx (806.77 kB)
Sensor Data Classification for the Indication of Lameness in Sheep
conference contribution
posted on 2023-08-30, 15:40 authored by Zainab Al-Rubaye, Ali Al-Sherbaz, Wanda McCormick, Scott TurnerLameness 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.
History
Volume
252Page range
309-320Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications EngineeringISSN
1867-8211External DOI
Publisher
SpringerPlace of publication
ChamISBN
978-3-030-00916-8Conference proceeding
Collaborative Computing: Networking, Applications and WorksharingName of event
13th International Conference, CollaborateCom 2017Location
Edinburgh, UKEvent start date
2013-12-11Event finish date
2013-12-13Editors
Imed Romdhani, Lei Shu, Hara Takahiro, Zhangbing Zhou, Timothy Gordon, Deze ZengFile version
- Accepted version
Language
- eng