Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

  • Guo Y
  • Poulton G
  • Corke P
 et al. 
  • 130

    Readers

    Mendeley users who have this article in their library.
  • 38

    Citations

    Citations of this article.

Abstract

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several "stay" areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows' movement between the "stay" areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species. © 2009 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Animal movement
  • Behaviour modelling
  • Hidden Markov models
  • Precision ranching
  • Sensor networks
  • Wireless

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Peter CorkeQueensland University of Technology

    Follow
  • Y. Guo

  • G. Poulton

  • G. J. Bishop-Hurley

  • T. Wark

  • D. L. Swain

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free