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Open AccessArticle

Predicting Lameness in Sheep Activity Using Tri-Axial Acceleration Signals

1
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
2
Sheep Cooperative Research Centre, University of New England, Armidale, NSW 2351, Australia
3
New South Wales Department of Primary Industries, Livestock Industries Centre, University of New England, Armidale, NSW 2351, Australia
4
Formerly Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
5
Institute for Future Farming Systems, School of Medical and Applied Sciences, Central Queensland University, Central Queensland Innovation and Research Precinct, Rockhampton, QLD 4702, Australia
*
Author to whom correspondence should be addressed.
Animals 2018, 8(1), 12; https://doi.org/10.3390/ani8010012
Received: 14 November 2017 / Revised: 22 December 2017 / Accepted: 6 January 2018 / Published: 11 January 2018
(This article belongs to the Special Issue Animal Management in the 21st Century)
Monitoring livestock farmed under extensive conditions is challenging and this is particularly difficult when observing animal behaviour at an individual level. Lameness is a disease symptom that has traditionally relied on visual inspection to detect those animals with an abnormal walking pattern. More recently, accelerometer sensors have been used in other livestock industries to detect lame animals. These devices are able to record changes in activity intensity, allowing us to differentiate between a grazing, walking, and resting animal. Using these on-animal sensors, grazing, standing, walking, and lame walking were accurately detected from an ear attached sensor. With further development, this classification algorithm could be linked with an automatic livestock monitoring system to provide real time information on individual health status, something that is practically not possible under current extensive livestock production systems.
Lameness is a clinical symptom associated with a number of sheep diseases around the world, having adverse effects on weight gain, fertility, and lamb birth weight, and increasing the risk of secondary diseases. Current methods to identify lame animals rely on labour intensive visual inspection. The aim of this current study was to determine the ability of a collar, leg, and ear attached tri-axial accelerometer to discriminate between sound and lame gait movement in sheep. Data were separated into 10 s mutually exclusive behaviour epochs and subjected to Quadratic Discriminant Analysis (QDA). Initial analysis showed the high misclassification of lame grazing events with sound grazing and standing from all deployment modes. The final classification model, which included lame walking and all sound activity classes, yielded a prediction accuracy for lame locomotion of 82%, 35%, and 87% for the ear, collar, and leg deployments, respectively. Misclassification of sound walking with lame walking within the leg accelerometer dataset highlights the superiority of an ear mode of attachment for the classification of lame gait characteristics based on time series accelerometer data. View Full-Text
Keywords: sheep; behavior; lameness; activity; acceleromter; on-animal sensor sheep; behavior; lameness; activity; acceleromter; on-animal sensor
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MDPI and ACS Style

Barwick, J.; Lamb, D.; Dobos, R.; Schneider, D.; Welch, M.; Trotter, M. Predicting Lameness in Sheep Activity Using Tri-Axial Acceleration Signals. Animals 2018, 8, 12. https://doi.org/10.3390/ani8010012

AMA Style

Barwick J, Lamb D, Dobos R, Schneider D, Welch M, Trotter M. Predicting Lameness in Sheep Activity Using Tri-Axial Acceleration Signals. Animals. 2018; 8(1):12. https://doi.org/10.3390/ani8010012

Chicago/Turabian Style

Barwick, Jamie; Lamb, David; Dobos, Robin; Schneider, Derek; Welch, Mitchell; Trotter, Mark. 2018. "Predicting Lameness in Sheep Activity Using Tri-Axial Acceleration Signals" Animals 8, no. 1: 12. https://doi.org/10.3390/ani8010012

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