Lameness detection in sheep through the analysis of the wireless sensor data

Research output: Contribution to conference typesPaperResearch

Abstract

Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy; therefore, the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling.
Original languageEnglish
Publication statusPublished - 14 Jun 2016
EventThe University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016 - Northampton
Duration: 14 Jun 2016 → …

Conference

ConferenceThe University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016
Period14/06/16 → …

Fingerprint

lameness
sensors (equipment)
sheep
sawing
culling (animals)
longitude
gait
posture
signs and symptoms (animals and humans)
neck
locomotion
data analysis
farming systems
farms
monitoring
animals

Cite this

Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D., & Turner, S. J. (2016). Lameness detection in sheep through the analysis of the wireless sensor data. Paper presented at The University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016, .
Al-Rubaye, Zainab ; Al-Sherbaz, Ali ; McCormick, Wanda D ; Turner, Scott J. / Lameness detection in sheep through the analysis of the wireless sensor data. Paper presented at The University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016, .
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abstract = "Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy; therefore, the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling.",
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year = "2016",
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note = "The University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016 ; Conference date: 14-06-2016",

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Al-Rubaye, Z, Al-Sherbaz, A, McCormick, WD & Turner, SJ 2016, 'Lameness detection in sheep through the analysis of the wireless sensor data' Paper presented at The University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016, 14/06/16, .

Lameness detection in sheep through the analysis of the wireless sensor data. / Al-Rubaye, Zainab; Al-Sherbaz, Ali; McCormick, Wanda D; Turner, Scott J.

2016. Paper presented at The University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016, .

Research output: Contribution to conference typesPaperResearch

TY - CONF

T1 - Lameness detection in sheep through the analysis of the wireless sensor data

AU - Al-Rubaye, Zainab

AU - Al-Sherbaz, Ali

AU - McCormick, Wanda D

AU - Turner, Scott J

PY - 2016/6/14

Y1 - 2016/6/14

N2 - Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy; therefore, the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling.

AB - Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy; therefore, the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling.

M3 - Paper

ER -

Al-Rubaye Z, Al-Sherbaz A, McCormick WD, Turner SJ. Lameness detection in sheep through the analysis of the wireless sensor data. 2016. Paper presented at The University of Northampton Graduate School Postgraduate Researcher (PGR) Conference 2016, .