School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington LE12 5RD, UK.
Internet of Things Systems Research, Intel Labs, W23 CX68 Leixlip, Ireland.
Sensors (Basel). 2019 Jul 20;19(14):3201. doi: 10.3390/s19143201.
Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to "concept drift", which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions.
精准养殖中的实时和长期行为监测系统具有改善福利和提高生产力的巨大潜力，从而促进农场动物的健康。然而，长期监测系统面临的一些最大挑战与“概念漂移”有关，当系统遇到具有挑战性的新条件或变化条件时，或者在训练数据不能准确反映实时感知数据的情况下，就会发生这种情况。本研究提出了一种结合离线算法和在线学习算法的方法来处理概念漂移，作者认为这是长期现场监测系统的一种有用机制。所提出的算法使用包含三轴加速度计和三轴陀螺仪传感器的嵌入式边缘设备中的信息来对三种相关羊行为进行分类。该方法首次应用于精准畜牧行为监测中，可实时在动态变化的条件下提高对羊的相关行为的分类能力。
