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.

Figure 1. Simple example illustrating KNN analysis when k = 3.

Here the new data points are classed according to a majority vote of their k nearest neighbours, so q1 is classed as red and q2 as blue. Two variables are used in this example, although the same approach may be used with n dimensional data such as tri-axial accelerometer data.

Figure 2. 3D Scatterplot showing raw tri-axial acceleration data for an Imperial cormorant (Phalacrocorax atriceps), data points are labelled by colour according to their behavioural classification.

Red – Ascent Phase of Dive, Green – Bottom Phase of Dive, Blue – Descent Phase of Dive, Purple – Flight, Black – Walking.