Abstract Inertial motion sensors located on the animal have been used to study the behaviour of ruminant livestock. The time window size of segmented signal data can significantly affect the classification accuracy of animal behaviours. To date, there have been no studies evaluating the impact of a mixture of time window size features on the accuracy of animal behaviour classification. In this study, data was collected from accelerometers attached to the neck of 17 Merino sheep over a period of two days. We also recorded a ground truth dataset of behaviour recordings (grazing, ruminating, walking, and standing) over the same time period, We then investigated the ability of three machine learning (ML) approaches, Random Forest (RF), Support Vector Machine (SVM) and linear discriminant analysis (LDA), to accurately classify sheep behaviour. Our results clearly show that simultaneous inclusion of features derived from time windows of mixed sizes, ranging from 2 to 15 s, significantly improved the behaviour classification accuracy, in comparison to those determined from a single unique time window size. Of the three ML methods applied here, the RF approach yielded the best results. Together our results show that including features obtained from mixed window sizes improved the classification accuracy of sheep behaviours.

中文翻译：

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包含来自混合时间窗口大小的特征提高了机器学习算法对绵羊放牧行为的分类精度

摘要 位于动物身上的惯性运动传感器已被用于研究反刍家畜的行为。分段信号数据的时间窗口大小会显着影响动物行为的分类准确性。迄今为止，还没有研究评估混合时间窗口大小特征对动物行为分类准确性的影响。在这项研究中，数据是在两天内从连接在 17 只美利奴羊脖子上的加速度计收集的。我们还记录了同一时间段内行为记录（放牧、反刍、行走和站立）的真实数据集，然后我们研究了三种机器学习 (ML) 方法的能力，随机森林 (RF)、支持向量机 ( SVM）和线性判别分析（LDA），以准确分类绵羊行为。我们的结果清楚地表明，与从单个唯一时间窗口大小确定的特征相比，同时包含从 2 到 15 秒的混合大小的时间窗口派生的特征，显着提高了行为分类的准确性。在此处应用的三种 ML 方法中，RF 方法产生了最好的结果。我们的结果一起表明，包括从混合窗口大小获得的特征提高了绵羊行为的分类准确性。