Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22834
Title: Predicting Lameness in Sheep Activity Using Tri-Axial Acceleration Signals
Contributor(s): Barwick, Jamie  (author)orcid ; Lamb, David  (author); Dobos, Robin C  (author)orcid ; Schneider, Derek  (author)orcid ; Welch, Mitchell  (author)orcid ; Trotter, Mark (author)
Publication Date: 2018
Open Access: Yes
DOI: 10.3390/ani8010012Open Access Link
Handle Link: https://hdl.handle.net/1959.11/22834
Abstract: 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.
Publication Type: Journal Article
Source of Publication: Animals, 8(1), p. 1-16
Publisher: MDPIAG
Place of Publication: Switzerland
ISSN: 2076-2615
Field of Research (FOR): 070203 Animal Management
Socio-Economic Outcome Codes: 830310 Sheep - Meat
830311 Sheep - Wool
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

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