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

Guo, Y., Poulton, G., Corke, Peter, Bishop-Hurley, G. J, Wark, T, & Swain, D. L (2009) Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model. Ecological Modelling, 220(17), 2068 - 2075.

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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.

Impact and interest:

20 citations in Scopus
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14 citations in Web of Science®

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ID Code: 33751
Item Type: Journal Article
Keywords: Precision ranching, Behaviour modelling, Wireless, Hidden Markov models, Sensor networks, Animal movement
DOI: 10.1016/j.ecolmodel.2009.04.047
ISSN: 03043800
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Deposited On: 19 Aug 2010 15:23
Last Modified: 01 Mar 2012 00:11

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