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Animal-borne behaviour classification for sheep (Dohne Merino) and rhinoceros (Ceratotherium simum and diceros bicornis)

dc.contributor.authorLe Roux, Solomon Petrusen_ZA
dc.contributor.authorMarias, Jacquesen_ZA
dc.contributor.authorWolhuter, Riaanen_ZA
dc.contributor.authorNiesler, Thomasen_ZA
dc.date.accessioned2017-11-27T06:06:19Z
dc.date.available2017-11-27T06:06:19Z
dc.date.issued2017-11-21
dc.identifier.citationLe Roux, S. P., et al. 2017. Animal-borne behaviour classification for sheep (Dohne Merino) and rhinoceros (Ceratotherium simum and diceros bicornis). Animal Biotelemetry, 5:25, doi:10.1186/s40317-017-0140-0
dc.identifier.issn2050-3385 (online)
dc.identifier.otherdoi:10.1186/s40317-017-0140-0
dc.identifier.urihttp://hdl.handle.net/10019.1/102512
dc.descriptionCITATION: Le Roux, S. P., et al. 2017. Animal-borne behaviour classification for sheep (Dohne Merino) and rhinoceros (Ceratotherium simum and diceros bicornis). Animal Biotelemetry, 5:25, doi:10.1186/s40317-017-0140-0.
dc.descriptionThe original publication is available at https://animalbiotelemetry.biomedcentral.com
dc.descriptionPublication of this article was funded by the Stellenbosch University Open Access Fund.
dc.description.abstractBackground: The ability to study animal behaviour is important in many fields of science, including biology, behavioural ecology and conservation. Behavioural information is usually obtained by attaching an electronic tag to the animal and later retrieving it to download the measured data. We present an animal-borne behaviour classification system, which captures and automatically classifies three-dimensional accelerometer data in real time. All computations occur on specially designed biotelemetry tags while attached to the animal. This allows the probable behaviour to be transmitted continuously, thereby providing an enhanced level of detail and immediacy. Results: The performance of the animal-borne automatic behaviour classification system is presented for sheep and rhinoceros. For sheep, a classification accuracy of 82.40% is achieved among five behavioural classes (standing, walking, grazing, running and lying down). For rhinoceros, an accuracy of 96.10% is achieved among three behavioural classes (standing, walking and lying down). The estimated behaviour was established approximately every 5.3 s for sheep and 6.5 s for rhinoceros. Conclusions: We demonstrate that accurate on-animal real-time behaviour classification is possible by successful design, implementation and deployed on sheep and rhinoceros. Since the bandwidth required to transmit the behaviour class is lower than that which would be required to transmit the accelerometer measurements themselves, this system is better suited to low-power and error-prone data communication channels that may be expected in the animals habitat.
dc.description.urihttps://animalbiotelemetry.biomedcentral.com/articles/10.1186/s40317-017-0140-0
dc.format.extent13 pages : illustrationsen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherBioMed Central
dc.subjectAccelerometersen_ZA
dc.subjectMachine learningen_ZA
dc.subjectRhinocerosen_ZA
dc.subjectBiotelemetryen_ZA
dc.subjectAnimals --Behaviour -- Classificationen_ZA
dc.titleAnimal-borne behaviour classification for sheep (Dohne Merino) and rhinoceros (Ceratotherium simum and diceros bicornis)en_ZA
dc.typeArticleen_ZA
dc.date.updated2017-11-26T04:58:03Z
dc.description.versionPublisher's version
dc.rights.holderAuthors retain copyright


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