Sensor data classification for the indication of lameness in sheep

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

doi:10.1007%2F978-3-030-00916-8_29

Sensor data classification for the indication of lameness in sheep

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

Research output: Contribution to Book/Report › Conference Contribution › peer-review

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.

Original language	English
Title of host publication	Collaborative Computing: Networking, Applications and Worksharing
Subtitle of host publication	13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings
Editors	Iman Romdhani, Lei Shu, Hara Takahiro, Zhangbing Zhou, Timothy Gordon, Deze Zeng
Place of Publication	Edinburgh
Publisher	Springer International Publishing
Pages	309-320
Number of pages	12
Edition	1
ISBN (Electronic)	9783030009168
ISBN (Print)	9783030009151
DOIs	https://doi.org/10.1007%2F978-3-030-00916-8_29
Publication status	E-pub ahead of print - 26 Sept 2018
Event	CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing - Edinburgh, United Kingdom Duration: 11 Dec 2017 → 13 Dec 2017 http://collaboratecom.eai-conferences.org/

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series
Volume	252

Conference

Conference	CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing
Country/Territory	United Kingdom
City	Edinburgh
Period	11/12/17 → 13/12/17
Internet address	http://collaboratecom.eai-conferences.org/

Keywords

sensor data analysis
classification
sheep lameness detection
machine learning

Access to Document

10.1007%2F978-3-030-00916-8_29

Zainab_etal_SPR_2018_Sensor_data_classification_for_the_indication_of_lameness_in_sheepAccepted author manuscript, 958 KBLicence: CC BY-NC-ND

Cite this

Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D., & Turner, S. J. (2018). Sensor data classification for the indication of lameness in sheep. In I. Romdhani, L. Shu, H. Takahiro, Z. Zhou, T. Gordon, & D. Zeng (Eds.), Collaborative Computing: Networking, Applications and Worksharing: 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings (1 ed., pp. 309-320). (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series; Vol. 252). Springer International Publishing. Advance online publication. https://doi.org/10.1007%2F978-3-030-00916-8_29

Al-Rubaye, Zainab ; Al-Sherbaz, Ali ; McCormick, Wanda D et al. / Sensor data classification for the indication of lameness in sheep. Collaborative Computing: Networking, Applications and Worksharing: 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings. editor / Iman Romdhani ; Lei Shu ; Hara Takahiro ; Zhangbing Zhou ; Timothy Gordon ; Deze Zeng. 1. ed. Edinburgh : Springer International Publishing, 2018. pp. 309-320 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series).

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title = "Sensor data classification for the indication of lameness in sheep",

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.",

keywords = "sensor data analysis, classification, sheep lameness detection, machine learning",

author = "Zainab Al-Rubaye and Ali Al-Sherbaz and McCormick, {Wanda D} and Turner, {Scott J}",

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doi = "10.1007%2F978-3-030-00916-8_29",

language = "English",

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series = "Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series",

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booktitle = "Collaborative Computing: Networking, Applications and Worksharing",

edition = "1",

note = "CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing ; Conference date: 11-12-2017 Through 13-12-2017",

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Al-Rubaye, Z, Al-Sherbaz, A, McCormick, WD & Turner, SJ 2018, Sensor data classification for the indication of lameness in sheep. in I Romdhani, L Shu, H Takahiro, Z Zhou, T Gordon & D Zeng (eds), Collaborative Computing: Networking, Applications and Worksharing: 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings. 1 edn, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series, vol. 252, Springer International Publishing, Edinburgh, pp. 309-320, CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, Edinburgh, United Kingdom, 11/12/17. https://doi.org/10.1007%2F978-3-030-00916-8_29

Sensor data classification for the indication of lameness in sheep. / Al-Rubaye, Zainab; Al-Sherbaz, Ali; McCormick, Wanda D et al.
Collaborative Computing: Networking, Applications and Worksharing: 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings. ed. / Iman Romdhani; Lei Shu; Hara Takahiro; Zhangbing Zhou; Timothy Gordon; Deze Zeng. 1. ed. Edinburgh: Springer International Publishing, 2018. p. 309-320 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series; Vol. 252).

Research output: Contribution to Book/Report › Conference Contribution › peer-review

TY - GEN

T1 - Sensor data classification for the indication of lameness in sheep

AU - Al-Rubaye, Zainab

AU - Al-Sherbaz, Ali

AU - McCormick, Wanda D

AU - Turner, Scott J

PY - 2018/9/26

Y1 - 2018/9/26

N2 - 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.

AB - 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.

KW - sensor data analysis

KW - classification

KW - sheep lameness detection

KW - machine learning

U2 - 10.1007%2F978-3-030-00916-8_29

DO - 10.1007%2F978-3-030-00916-8_29

M3 - Conference Contribution

SN - 9783030009151

T3 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series

SP - 309

EP - 320

BT - Collaborative Computing: Networking, Applications and Worksharing

A2 - Romdhani, Iman

A2 - Shu, Lei

A2 - Takahiro, Hara

A2 - Zhou, Zhangbing

A2 - Gordon, Timothy

A2 - Zeng, Deze

PB - Springer International Publishing

CY - Edinburgh

T2 - CollaborateCom 2017 - 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing

Y2 - 11 December 2017 through 13 December 2017

ER -

Al-Rubaye Z, Al-Sherbaz A, McCormick WD, Turner SJ. Sensor data classification for the indication of lameness in sheep. In Romdhani I, Shu L, Takahiro H, Zhou Z, Gordon T, Zeng D, editors, Collaborative Computing: Networking, Applications and Worksharing: 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings. 1 ed. Edinburgh: Springer International Publishing. 2018. p. 309-320. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series). Epub 2018 Sept 26. doi: 10.1007%2F978-3-030-00916-8_29

Sensor data classification for the indication of lameness in sheep

Abstract

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Conference

Keywords

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