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.

Use of multiple modes of flight subsidy by a soaring terrestrial bird, the golden eagleAquila chrysaetos, when on migration

“…Our second step involved using the remaining standardized predictors (those for which F-tests were statistically significant) in a wkNN analysis (R v. 2.15.2, package kknn; [24,25]) to model Table 1. Data types collected (first three variables) or interpreted (remainder) from high-frequency GPS -GSM telemetry systems on 32 golden eagles migrating through the central Appalachian Mountains.…”

Using accelerometers to remotely and automatically characterize behavior in small animals

“…Many simultaneous recordings of paired behavioral observations and accelerometer readings must be collected to determine the correct mapping. Machine-learning techniques have successfully been used to complete this task (Carroll et al, 2014;Bidder et al, 2014;Escalante et al, 2013;Gao et al, 2013;Grünewälder et al, 2012;Martiskainen et al, 2009;McClune et al, 2014;Nathan et al, 2012;Sakamoto et al, 2009). Previously, machine-learning algorithms have not modeled sequential correlations between behaviors and have not allowed for flexible lengths of behavioral segments, two constraints that may limit system accuracy.…”

Overinflated behavioural energetics: using dynamic body acceleration to accurately measure behaviour duration and estimate energy expenditure

“…Furthermore, behaviour duration cannot be calculated accurately when acceleration is measured in blocks of 3 s (ShamounBaranes et al 2012), especially when behaviour durations are short, e.g. <10 s. Automatic animal behavioural classification of acceleration data using the k-nearest neighbour algorithm (Bidder et al 2014) can be much improved by analysing behaviours of short duration because, as this report highlights, shellfish (invertebrate) and human (vertebrate) behaviours can frequently be <1 to 10 s in duration.…”