Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Use of accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.

在绵羊养殖国家中，绵羊跛行是影响其健康与福利的最主要关切问题。跛行防控的最佳实践依赖于快速治疗，但目前尚无用于跛行检测的客观手段。加速度计（accelerometer）与陀螺仪（gyroscope）已被广泛应用于人类活动研究，且在畜禽领域的应用也日趋普及。本研究采用基于加速度计与陀螺仪的耳部传感器采集的23组数据集（其中健康绵羊10只、跛行绵羊13只），采样频率为16 Hz，旨在开发并对比可区分三种不同行为状态（行走、站立与躺卧）下跛行状态的算法。本研究首次证实，从加速度计与陀螺仪信号中提取的特征，可在站立、行走与躺卧三种状态下区分跛行与健康绵羊。随机森林（Random Forest）算法在跛行分类任务中表现最优：躺卧状态下分类准确率达84.91%，站立状态下为81.15%，行走状态下为76.83%；各行为状态下的整体分类准确率均超过80%。基于加速度计与陀螺仪的特征均位列分类任务的前十重要特征之列。本研究结果表明，跛行与健康绵羊在三种行为状态下存在的新型行为差异，可用于开发跛行检测的自动化系统。