The use of multivariable wireless sensor data to early detect lameness in sheep

Research output: Contribution to conference typesPaperResearch

Abstract

Lameness is a clinical symptom of the painful disorder, which refers to the locomotion changes in sheep movement. These unbalanced movements result in a deviation from normal gait or posture. The footrot is considered one of the most significant causes of lameness in sheep in Great Britain due to a bacteria grows in a mud soil which transfer to the sheep foot and cause footrot that leads to lameness. Lameness has a negative impact on both sheep welfare and farm economy. The annual loss from the footrot only is estimated by £6 for each ewe in Great Britain according to the statistics from Agriculture and Horticulture Development Board (AHDB) in 2014. Therefore, preclinical detection of lameness at the farm will increase the level of protection regarding sheep health and farm commerce decline. The newly developed sensor technology utilises the idea of automatically monitoring objects either human or animal to determine the physiological and behavioural indicators, which are subsequently used an input to data analysis algorithms. The automated methods to monitor the farm bring many advantages to the farmer in terms of time spending, flock size increasing and sensitivity to detect the lameness The type of the collected data from the sensor used for recording animal’s behaviour depend on the sensor’s features and functionality. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides 3-aix acceleration, 3-aix angular velocity, 3-aix angles (Roll, Pitch, and Heading), longitude, latitude and time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to early detect lameness in sheep by analysing the data that will be retrieved from a mounted sensor on the sheep neck collar. This extensive spatio-temporal data will be classified to infer the associated behaviour to the lame sheep according to an efficient data mining learning techniques. This model will help the shepherd to early detect the lame sheep to prevent the worse situation of trimming or even culling the sheep.
Original languageEnglish
Publication statusPublished - 2 Mar 2016
EventSchool of Science and Technology Annual Research Conference - Newton Building, The University of Northampton
Duration: 2 Mar 2016 → …

Conference

ConferenceSchool of Science and Technology Annual Research Conference
Period2/03/16 → …

Fingerprint

lameness
sheep
sensors (equipment)
footrot
farms
sawing
culling (animals)
monitoring
longitude
gait
horticulture
collars
posture
heading
animal behavior
signs and symptoms (animals and humans)
neck
locomotion
ewes
data analysis

Cite this

Al-Rubaye, Z., Al-Sherbaz, A., McCormick, W. D., & Turner, S. J. (2016). The use of multivariable wireless sensor data to early detect lameness in sheep. Paper presented at School of Science and Technology Annual Research Conference, .
Al-Rubaye, Zainab ; Al-Sherbaz, Ali ; McCormick, Wanda D ; Turner, Scott J. / The use of multivariable wireless sensor data to early detect lameness in sheep. Paper presented at School of Science and Technology Annual Research Conference, .
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title = "The use of multivariable wireless sensor data to early detect lameness in sheep",
abstract = "Lameness is a clinical symptom of the painful disorder, which refers to the locomotion changes in sheep movement. These unbalanced movements result in a deviation from normal gait or posture. The footrot is considered one of the most significant causes of lameness in sheep in Great Britain due to a bacteria grows in a mud soil which transfer to the sheep foot and cause footrot that leads to lameness. Lameness has a negative impact on both sheep welfare and farm economy. The annual loss from the footrot only is estimated by £6 for each ewe in Great Britain according to the statistics from Agriculture and Horticulture Development Board (AHDB) in 2014. Therefore, preclinical detection of lameness at the farm will increase the level of protection regarding sheep health and farm commerce decline. The newly developed sensor technology utilises the idea of automatically monitoring objects either human or animal to determine the physiological and behavioural indicators, which are subsequently used an input to data analysis algorithms. The automated methods to monitor the farm bring many advantages to the farmer in terms of time spending, flock size increasing and sensitivity to detect the lameness The type of the collected data from the sensor used for recording animal’s behaviour depend on the sensor’s features and functionality. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides 3-aix acceleration, 3-aix angular velocity, 3-aix angles (Roll, Pitch, and Heading), longitude, latitude and time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to early detect lameness in sheep by analysing the data that will be retrieved from a mounted sensor on the sheep neck collar. This extensive spatio-temporal data will be classified to infer the associated behaviour to the lame sheep according to an efficient data mining learning techniques. This model will help the shepherd to early detect the lame sheep to prevent the worse situation of trimming or even culling the sheep.",
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year = "2016",
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day = "2",
language = "English",
note = "School of Science and Technology Annual Research Conference ; Conference date: 02-03-2016",

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Al-Rubaye, Z, Al-Sherbaz, A, McCormick, WD & Turner, SJ 2016, 'The use of multivariable wireless sensor data to early detect lameness in sheep' Paper presented at School of Science and Technology Annual Research Conference, 2/03/16, .

The use of multivariable wireless sensor data to early detect lameness in sheep. / Al-Rubaye, Zainab; Al-Sherbaz, Ali; McCormick, Wanda D; Turner, Scott J.

2016. Paper presented at School of Science and Technology Annual Research Conference, .

Research output: Contribution to conference typesPaperResearch

TY - CONF

T1 - The use of multivariable wireless sensor data to early detect lameness in sheep

AU - Al-Rubaye, Zainab

AU - Al-Sherbaz, Ali

AU - McCormick, Wanda D

AU - Turner, Scott J

PY - 2016/3/2

Y1 - 2016/3/2

N2 - Lameness is a clinical symptom of the painful disorder, which refers to the locomotion changes in sheep movement. These unbalanced movements result in a deviation from normal gait or posture. The footrot is considered one of the most significant causes of lameness in sheep in Great Britain due to a bacteria grows in a mud soil which transfer to the sheep foot and cause footrot that leads to lameness. Lameness has a negative impact on both sheep welfare and farm economy. The annual loss from the footrot only is estimated by £6 for each ewe in Great Britain according to the statistics from Agriculture and Horticulture Development Board (AHDB) in 2014. Therefore, preclinical detection of lameness at the farm will increase the level of protection regarding sheep health and farm commerce decline. The newly developed sensor technology utilises the idea of automatically monitoring objects either human or animal to determine the physiological and behavioural indicators, which are subsequently used an input to data analysis algorithms. The automated methods to monitor the farm bring many advantages to the farmer in terms of time spending, flock size increasing and sensitivity to detect the lameness The type of the collected data from the sensor used for recording animal’s behaviour depend on the sensor’s features and functionality. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides 3-aix acceleration, 3-aix angular velocity, 3-aix angles (Roll, Pitch, and Heading), longitude, latitude and time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to early detect lameness in sheep by analysing the data that will be retrieved from a mounted sensor on the sheep neck collar. This extensive spatio-temporal data will be classified to infer the associated behaviour to the lame sheep according to an efficient data mining learning techniques. This model will help the shepherd to early detect the lame sheep to prevent the worse situation of trimming or even culling the sheep.

AB - Lameness is a clinical symptom of the painful disorder, which refers to the locomotion changes in sheep movement. These unbalanced movements result in a deviation from normal gait or posture. The footrot is considered one of the most significant causes of lameness in sheep in Great Britain due to a bacteria grows in a mud soil which transfer to the sheep foot and cause footrot that leads to lameness. Lameness has a negative impact on both sheep welfare and farm economy. The annual loss from the footrot only is estimated by £6 for each ewe in Great Britain according to the statistics from Agriculture and Horticulture Development Board (AHDB) in 2014. Therefore, preclinical detection of lameness at the farm will increase the level of protection regarding sheep health and farm commerce decline. The newly developed sensor technology utilises the idea of automatically monitoring objects either human or animal to determine the physiological and behavioural indicators, which are subsequently used an input to data analysis algorithms. The automated methods to monitor the farm bring many advantages to the farmer in terms of time spending, flock size increasing and sensitivity to detect the lameness The type of the collected data from the sensor used for recording animal’s behaviour depend on the sensor’s features and functionality. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides 3-aix acceleration, 3-aix angular velocity, 3-aix angles (Roll, Pitch, and Heading), longitude, latitude and time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to early detect lameness in sheep by analysing the data that will be retrieved from a mounted sensor on the sheep neck collar. This extensive spatio-temporal data will be classified to infer the associated behaviour to the lame sheep according to an efficient data mining learning techniques. This model will help the shepherd to early detect the lame sheep to prevent the worse situation of trimming or even culling the sheep.

M3 - Paper

ER -

Al-Rubaye Z, Al-Sherbaz A, McCormick WD, Turner SJ. The use of multivariable wireless sensor data to early detect lameness in sheep. 2016. Paper presented at School of Science and Technology Annual Research Conference, .