Barwick Jamie, Lamb David, Dobos Robin, Schneider Derek, Welch Mitchell, Trotter Mark
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia.
Sheep Cooperative Research Centre, University of New England, Armidale, NSW 2351, Australia.
Animals (Basel). 2018 Jan 11;8(1):12. doi: 10.3390/ani8010012.
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.
跛行是一种在世界各地与多种绵羊疾病相关的临床症状，会对体重增加、繁殖力和羔羊出生体重产生不利影响，并增加继发疾病的风险。目前识别跛脚动物的方法依赖于劳动强度大的目视检查。本研究的目的是确定一种安装在项圈、腿部和耳部的三轴加速度计区分绵羊正常步态和跛行步态运动的能力。数据被分成10秒相互排斥的行为时段，并进行二次判别分析（QDA）。初步分析表明，在所有部署模式下，跛行放牧事件与正常放牧和站立之间存在高度误分类。最终的分类模型包括跛行行走和所有正常活动类别，对于耳部、项圈和腿部部署，跛行运动的预测准确率分别为82%、35%和87%。腿部加速度计数据集中正常行走与跛行行走的误分类突出了基于时间序列加速度计数据，耳部安装模式在跛行步态特征分类方面的优越性。
