Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D. and Turner, S. J.
CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing.
Edinburgh: Springer .
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
citation: Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D. and Turner, S. J. (2017) Sensor data classification for the indication of lameness in sheep. In: CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing. Edinburgh: Springer .
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