Abstract
Increasingly animal behaviour studies are enhanced through the use of
accelerometry. To allow translation of raw accelerometer data to animal
behaviours requires the development of classifiers. Here, we present the
“rabc” package to assist researchers with the interactive development
of such animal-behaviour classifiers based on datasets consisting out of
accelerometer data with their corresponding animal behaviours. Using an
accelerometer and a corresponding behavioural dataset collected on white
stork (Ciconia ciconia), we illustrate the workflow of this package,
including raw data visualization, feature calculation, feature
selection, feature visualization, extreme gradient boost model training,
validation, and, finally, a demonstration of the behaviour
classification results.