Unsupervised automated monitoring of dairy cows' behavior based on Inertial Measurement Unit attached to their back

摘要

Automated monitoring of dairy cow behavior based on non-invasive sensors offers a great potential to improve the monitoring processes of its welfare and health in the context of the smart farm. It can detect any changes before the appearance of the clinical signs, allowing the farmer to take necessary measures as soon as possible. The objective of this study is to develop an effective un-supervised classification model of data collected by Inertial Measurement Units (IMU) attached to the back of dairy cows housed in free-stall. These data were aggregated according to different sampling frequencies and segmentation windows. The different times of lying, standing, lying down, standing up, walking and stationary behaviors were observed and recorded in real time. The designed classification model is based on univariate and multivariate Finite Mixture Models (FMM) and decision trees. The valid transitions between standing and lying behaviors are guaranteed by constraints imposed by a deterministic finite state automaton. The obtained results revealed that 99% of behaviors are well classified. Standing, lying on each side and changing between these positions are classified with 100% accuracy, followed by stationary with 99% sensitivity, 96% specificity, 99% precision and 99% accuracy. The walking behavior is classified with 96% sensitivity, 99% specificity, 91% precision and 98% accuracy. These results show that the back is an interesting location for sensors to monitor the dairy cow behavior.