Northampton Electronic Collection of Theses and Research

Sensor data classification for the indication of lameness in sheep

Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D. and Turner, S. J. Sensor data classification for the indication of lameness in sheep. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T. and Zeng, D. (eds.) Collaborative Computing: Networking, Applications and Worksharing : 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings. GBR: Springer International Publishing. pp. 309-320.

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[thumbnail of Zainab_etal_SPR_2018_Sensor_data_classification_for_the_indication_of_lameness_in_sheep]
Zainab_etal_SPR_2018_Sensor_data_classification_for_the_indication_of_lameness_in_sheep
Zainab_etal_SPR_2018_Sensor_data_classification_for_the_indication_of_lameness_in_sheep.pdf - Accepted Version
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Information
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.
Uncontrolled Keywords:
sensor data analysis, classification, sheep lameness detection, machine learning
Creators:
Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D. and Turner, S. J.
Editors:
Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T. and Zeng, D.
Publisher:
Springer International Publishing
Faculties, Divisions and Institutes:
Faculties > Faculty of Arts, Science & Technology > Computing
Research Centres > Centre for Physical Activity and Life Sciences
Page Range:
pp. 309-320
Title of Book:
Collaborative Computing: Networking, Applications and Worksharing : 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings
Series Name:
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series
Number:
1
Event Title:
CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing
Event Dates:
2017-12-11 - 2017-12-13
Place of Publication:
GBR
Number of Pages:
12
Language:
English
ISBN:
9783030009151
DOI:
10.1007/978-3-030-00916-8_29
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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