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dc.contributor.author
Bidder, Owen R.
dc.contributor.author
Campbell, Hamish A.
dc.contributor.author
Gómez Laich, Agustina Marta
dc.contributor.author
Urgé, Patricia
dc.contributor.author
Walker, James
dc.contributor.author
Cai, Yuzhi
dc.contributor.author
Gao, Lianli
dc.contributor.author
Quintana, Flavio Roberto
dc.contributor.author
Wilson, Rory P.
dc.date.available
2017-06-01T13:33:58Z
dc.date.issued
2014-02-21
dc.identifier.citation
Bidder, Owen R.; Campbell, Hamish A.; Gómez Laich, Agustina Marta; Urgé, Patricia; Walker, James; et al.; Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-Nearest neighbour algorithm; Public Library Of Science; Plos One; 9; 2; 21-2-2014; 1-7
dc.identifier.issn
1932-6203
dc.identifier.uri
http://hdl.handle.net/11336/17261
dc.description.abstract
Researchers hoping to elucidate the behaviour of species that aren’t readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Public Library Of Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Body Acceleration
dc.subject
Energy-Expenditure
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Adeline Penguins
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Locomotion
dc.subject
Ecology
dc.subject
Accelerometer
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System
dc.subject
Speed
dc.subject.classification
Ecología
dc.subject.classification
Ciencias Biológicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-Nearest neighbour algorithm
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-05-29T15:47:19Z
dc.journal.volume
9
dc.journal.number
2
dc.journal.pagination
1-7
dc.journal.pais
Estados Unidos
dc.journal.ciudad
San Francisco
dc.description.fil
Fil: Bidder, Owen R.. Swansea University; Reino Unido
dc.description.fil
Fil: Campbell, Hamish A.. The University Of Queensland; Australia
dc.description.fil
Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
dc.description.fil
Fil: Urgé, Patricia. Swansea University; Reino Unido
dc.description.fil
Fil: Walker, James. Swansea University; Reino Unido
dc.description.fil
Fil: Cai, Yuzhi. Swansea University; Reino Unido
dc.description.fil
Fil: Gao, Lianli. The University Of Queensland; Australia
dc.description.fil
Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
dc.description.fil
Fil: Wilson, Rory P.. Swansea University; Reino Unido
dc.journal.title
Plos One
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1371/journal.pone.0088609
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088609


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