Grünewälder, S;
Broekhuis, F;
Macdonald, DW;
Wilson, AM;
McNutt, JW;
Shawe-Taylor, J;
Hailes, S;
(2012)
Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus).
PLoS One
, 7
(11)
, Article e49120. 10.1371/journal.pone.0049120.
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Abstract
We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be 83%-94%, but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.
Type: | Article |
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Title: | Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus). |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1371/journal.pone.0049120 |
Publisher version: | http://dx.doi.org/10.1371/journal.pone.0049120 |
Language: | English |
Additional information: | © 2012 Grünewälder et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The authors had financial support from numerous donors including the Tom Kaplan Prize Scholarship, the PG Allen Family Foundation, the Lee and Juliet Folger Foundation, Nathan and Rosemarie Myhrvold, Stuart and Teresa Graham, Doug and Janet True, Woodland Park Zoo, Wild Entrust International. This work was funded by the EPSRC CARDyAL: Cooperative Aerodynamics and Radio-based DYnamic Animal Localisation project, EP/H017402/1. The authors confirm that there are no additional funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1378759 |
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