AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements.
Yehezkel S Resheff1, Shay Rotics2, Roi Harel2
1Movement 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.
Movement Ecology
|February 25, 2015
Summary
AcceleRater is a new tool that uses acceleration data to classify animal behavior, making movement ecology research more accessible. This Python application helps researchers easily train and use models for behavior identification.
Area of Science:
- Animal behavior
- Movement ecology
- Bio-logging technology
Background:
- Technological advancements are driving progress in animal movement studies.
- Biologgers with acceleration (ACC) recordings are valuable for estimating energy expenditure and identifying behaviors.
- Supervised learning shows promise for classifying behaviors from acceleration data, but implementation is hindered by technical challenges.
Purpose of the Study:
- To develop a broadly applicable tool for classifying animal behavior from acceleration data.
- To provide a user-friendly application for supervised learning of behavioral modes.
Main Methods:
- Introduction of AcceleRater, a free, Python-based web application.
- Utilizing AcceleRater for supervised learning of behavioral modes from ACC measurements.
- Application demonstrated on classifying vulture behavioral modes from acceleration data.
Main Results:
- AcceleRater achieved accuracies between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network).
- The mean overall accuracy across seven models was 81.51% (SD 3.95%).
- Performance variation was greater between behavioral modes than between different classification models.
Conclusions:
- AcceleRater offers a user-friendly tool for ACC-based behavioral annotation.
- The application facilitates the identification of animal behavior from acceleration data.
- AcceleRater will be dynamically upgraded and maintained for continued utility.
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