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

Movement Ecology

Ran Nathan¹  Orr Spiegel³  Roi Harel¹  Shay Rotics¹  Yehezkel S Resheff² 

[1] Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel;Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem 91904, Israel;Present address: Department of Environmental Science & Policy, University of California at Davis, Davis 95616, CA, USA

关键词: Web application;    Tri-axial acceleration;    Supervised learning;    Movement ecology;    Ethology;    Classification;    Biologging;    Animal behavior;    AcceleRater;   

Others  :  1132167
DOI  :  10.1186/s40462-014-0027-0

 received in 2014-09-08, accepted in 2014-12-15,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Resheff et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

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