AMoveE 2014: Joan Garriga
Channel: Movebank
Date: 2014-05-15

This tutorial was presented by Joan Garriga on 7 May 2014 as part of the Symposium on Animal Movement and the Environment, held at the North Carolina Museum of Natural Sciences in Raleigh, NC.

Garriga J, Bartumeus F, 2014, "EMbC: an algorithm for behavioral annotation of animal movement"

Abstract: The growing capacity to process and store animal tracks has spurred the development of new methods to segment animal trajectories into their elementary units of movement: behavioural modes. Key challenges for movement trajectory segmentation are to (i) minimize the need for supervision, (ii) limit analytical complexity (number of parameters and prior assumptions), (iii) avoid sensitivity to prior assumptions and/or initial values, and (iv) capture sufficiently general and biologically meaningfully semantics.
We present the EMbC algorithm (Expectation-Maximization binary Clustering) for behavioural annotation. This algorithm is a variant of the Gaussian Mixture Model maximum likelihood estimation algorithm, also known as Expectation-Maximization Clustering.
The EMbC algorithm fills a gap in movement trajectory segmentation procedures by reaching a good compromise between meaningful and easily interpretable behavioural segmentation and sound (and robust) statistical performance. As an unsupervised and non-intensive computing method, the EMbC algorithm is particularly suited for big data and large scale analyses where comparisons across species, sampling schemes, tracking technologies, and ecological contexts are looked for.