Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics. The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%. These results show that 85% of this model’s predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows.

中文翻译：

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使用自动记录的爱尔兰产后奶牛活动、行为和生产数据预测跛行

尽管视觉运动评分便宜且简单，但它也耗时且主观。自动跛行检测方法已经被开发出来，以取代视觉运动评分，并有助于早期和准确的检测。有几种类型的传感器正在测量活动、说谎行为或温度等特征。先前关于自动跛行检测的研究无法与农场商业环境中的实际实施相结合实现高精度。我们研究的目的是结合远程传感器技术和其他动物记录来开发奶牛跛行的预测模型，将传感器数据转换为农民易于解释的分类运动信息。在 11 个月的时间里，收集了爱尔兰研究农场 164 头荷斯坦-弗里斯兰奶牛的数据。颈部安装的加速度计用于收集行为指标，其他自动记录的数据包括产奶量和活重。运动评分数据采用一到五的评分标准（1=非跛行，5=严重跛行）手动记录。然后使用运动得分将奶牛标记为健全（运动得分 1）或不健全（运动得分≥2）。使用梯度提升决策树机器学习算法构建了四个监督分类模型，以研究奶牛是否可以被分类为健康或不健康。可用于模型构建的数据包括行为指标、产奶量和动物特征。最终的模型是使用数据源的各种组合构建的。然后使用混淆矩阵、接收者-操作者特征曲线和校准图来比较模型的准确性。根据准确度测量，实现最高性能的模型是结合了所有可用数据的模型，导致曲线下面积为 85%，敏感性和特异性为 78%。这些结果表明，该模型在识别奶牛健康或不健康方面的预测有 85% 是正确的，这表明使用颈部安装的加速度计，结合生产和其他动物数据，有可能取代视觉运动评分作为跛行检测奶牛的方法。