title: Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm.
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.
authors.affiliation: College of Science, Swansea University, Swansea, Wales.
authors.forename: Owen R
authors.initials: OR
authors.lastname: Bidder
authors.affiliation: School of Biological Sciences, University of Queensland Brisbane, Queensland, Australia.
authors.forename: Hamish A
authors.initials: HA
authors.lastname: Campbell
authors.affiliation: Centro Nacional Patagónico - Consejo Nacional de Investigaciones Cientificas y Técnias, Puerto Madryn, Chubut, Argentina.
authors.forename: Agustina
authors.initials: A
authors.lastname: Gómez-Laich
authors.affiliation: College of Science, Swansea University, Swansea, Wales.
authors.forename: Patricia
authors.initials: P
authors.lastname: Urgé
authors.affiliation: College of Engineering, Swansea University, Swansea, Wales.
authors.forename: James
authors.initials: J
authors.lastname: Walker
authors.affiliation: School of Management, Swansea University, Swansea, Wales.
authors.forename: Yuzhi
authors.initials: Y
authors.lastname: Cai
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland Brisbane, Queensland, Australia.
authors.forename: Lianli
authors.initials: L
authors.lastname: Gao
authors.affiliation: Centro Nacional Patagónico - Consejo Nacional de Investigaciones Cientificas y Técnias, Puerto Madryn, Chubut, Argentina ; Wildlife Conservation Society, Ciudad de Buenos Aires, Argentina.
authors.forename: Flavio
authors.initials: F
authors.lastname: Quintana
authors.affiliation: College of Science, Swansea University, Swansea, Wales.
authors.forename: Rory P
authors.initials: RP
authors.lastname: Wilson
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal / Q000502:physiology*; D002965:Classification / Q000379:methods; D009043:Motor Activity / Q000502:physiology*; D013045:Species Specificity; D013686:Telemetry / Q000295:instrumentation / Q000379:methods*