BioSense Institute, 21101 Novi Sad, Serbia.
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1RD, UK.
Sensors (Basel). 2022 Mar 17;22(6):2323. doi: 10.3390/s22062323.
Monitoring and classification of dairy cattle behaviours is essential for optimising milk yields. Early detection of illness, days before the critical conditions occur, together with automatic detection of the onset of oestrus cycles is crucial for obviating prolonged cattle treatments and improving the pregnancy rates. Accelerometer-based sensor systems are becoming increasingly popular, as they are automatically providing information about key cattle behaviours such as the level of restlessness and the time spent ruminating and eating, proxy measurements that indicate the onset of heat events and overall welfare, at an individual animal level. This paper reports on an approach to the development of algorithms that classify key cattle states based on a systematic dimensionality reduction process through two feature selection techniques. These are based on Mutual Information and Backward Feature Elimination and applied on knowledge-specific and generic time-series extracted from raw accelerometer data. The extracted features are then used to train classification models based on a Hidden Markov Model, Linear Discriminant Analysis and Partial Least Squares Discriminant Analysis. The proposed feature engineering methodology permits model deployment within the computing and memory restrictions imposed by operational settings. The models were based on measurement data from 18 steers, each animal equipped with an accelerometer-based neck-mounted collar and muzzle-mounted halter, the latter providing the truthing data. A total of 42 time-series features were initially extracted and the trade-off between model performance, computational complexity and memory footprint was explored. Results show that the classification model that best balances performance and computation complexity is based on Linear Discriminant Analysis using features selected through Backward Feature Elimination. The final model requires 1.83 ± 1.00 ms to perform feature extraction with 0.05 ± 0.01 ms for inference with an overall balanced accuracy of 0.83.
监测和分类奶牛行为对于优化牛奶产量至关重要。在关键条件发生之前及早发现疾病，以及自动检测发情周期的开始，对于避免长期的牛治疗和提高怀孕率至关重要。基于加速度计的传感器系统越来越受欢迎，因为它们可以自动提供有关关键牛行为的信息，例如不安定程度、反刍和进食时间，这些是表示发情事件和整体福利的代理测量，可在个体动物水平上进行。本文介绍了一种基于系统降维过程的算法开发方法，该方法通过两种特征选择技术来对关键牛状态进行分类。这些技术基于互信息和后向特征消除，并应用于从原始加速度计数据中提取的特定于知识和通用的时间序列。然后，使用提取的特征来基于隐马尔可夫模型、线性判别分析和偏最小二乘判别分析来训练分类模型。所提出的特征工程方法允许在操作设置施加的计算和内存限制内部署模型。该模型基于来自 18 头公牛的数据，每头动物都配备了基于加速度计的颈戴项圈和口鼻戴马嚼子，后者提供真实数据。最初提取了 42 个时间序列特征，并探讨了模型性能、计算复杂性和内存占用之间的权衡。结果表明，基于后向特征消除选择特征的线性判别分析分类模型在性能和计算复杂性之间取得了最佳平衡。最终模型执行特征提取需要 1.83 ± 1.00 毫秒，推理需要 0.05 ± 0.01 毫秒，整体平衡准确率为 0.83。
