Deep transfer learning in sheep activity recognition using accelerometer data
作者：
Highlights：
• Monitoring animals through human observation is time consuming and labour intensive.
• Smart devices mounted on the animals, embedded with predictive models are needed.
• Dataset diversity using two types of accelerometers attached to animals’ collars.
• Convolutional Neural Networks to predict active, grazing, and inactive behaviours.
• Use of Transfer learning to evaluate the generalization of the pre-trained model.
摘要
•Monitoring animals through human observation is time consuming and labour intensive.•Smart devices mounted on the animals, embedded with predictive models are needed.•Dataset diversity using two types of accelerometers attached to animals’ collars.•Convolutional Neural Networks to predict active, grazing, and inactive behaviours.•Use of Transfer learning to evaluate the generalization of the pre-trained model.
论文关键词：Accelerometer sensors,Animal activity recognition,Convolutional neural networks,Deep learning,Sheep behavior,Transfer learning
论文评审过程：Received 29 November 2020, Revised 27 May 2022, Accepted 18 June 2022, Available online 26 June 2022, Version of Record 28 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117925
