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Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

Overview of attention for article published in PLoS ONE, April 2017
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Title
Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds
Published in
PLoS ONE, April 2017
DOI 10.1371/journal.pone.0174785
Pubmed ID
Authors

Maitreyi Sur, Tony Suffredini, Stephen M. Wessells, Peter H. Bloom, Michael Lanzone, Sheldon Blackshire, Srisarguru Sridhar, Todd Katzner

Abstract

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 25%
Researcher 13 24%
Student > Ph. D. Student 11 20%
Student > Bachelor 4 7%
Student > Doctoral Student 3 5%
Other 6 11%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 44%
Environmental Science 9 16%
Engineering 4 7%
Neuroscience 2 4%
Medicine and Dentistry 2 4%
Other 9 16%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 April 2017.
All research outputs
#8,450,317
of 9,689,121 outputs
Outputs from PLoS ONE
#106,036
of 124,931 outputs
Outputs of similar age
#221,175
of 263,538 outputs
Outputs of similar age from PLoS ONE
#3,842
of 4,514 outputs
Altmetric has tracked 9,689,121 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 4,514 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.