AcceleRater: A web application for supervised learning of behavioral modes from acceleration measurements

Yehezkel S. Resheff*, Shay Rotics, Roi Harel, Orr Spiegel, Ran Nathan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


Background: The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. Description: Here we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models. Conclusions: AcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.

Original languageAmerican English
Article number27
JournalMovement Ecology
Issue number1
StatePublished - 25 Dec 2014

Bibliographical note

Publisher Copyright:
© 2014 Resheff et al.; licensee BioMed Central.


  • AcceleRater
  • Animal behavior
  • Biologging
  • Classification
  • Ethology
  • Movement ecology
  • Supervised learning
  • Tri-axial acceleration
  • Web application


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