Owen R. Bidder

TL;DR: A method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN), and 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.

TL;DR: This work used both empirical- and modelling-based approaches to show that the energetic costs for turns in both terrestrial and aerial locomotion are substantial, which calls into question the value of conventional movement models such as correlated random walk or Lévy walk for assessing optimum path types.

TL;DR: This study is the first explicit demonstration of terrestrial dead-reckoning, which provides a workable method of deriving the paths of animals on a step-by-step scale, and the wider implications for the understanding of animal movement ecology are discussed.

TL;DR: The use of a tame surrogate (domestic dog) is explored to build a behavioural classification module, and that module was used to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species.

TL;DR: Framework4 is an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves.