Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/16160
Title: Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm
Contributor(s): Bidder, Owen R (author); Campbell, Hamish  (author); Gomez-Laich, Agustina (author); Urge, Patricia (author); Walker, James (author); Cai, Yuzhi (author); Gao, Lianli (author); Quintana, Flavio (author); Wilson, Rory P (author)
Publication Date: 2014
Open Access: Yes
DOI: 10.1371/journal.pone.0088609Open Access Link
Handle Link: https://hdl.handle.net/1959.11/16160
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.
Publication Type: Journal Article
Source of Publication: PLoS One, 9(2), p. 1-7
Publisher: Public Library of Science (PLoS)
Place of Publication: United States of America
ISSN: 1932-6203
Field of Research (FOR): 050211 Wildlife and Habitat Management
060899 Zoology not elsewhere classified
060201 Behavioural Ecology
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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