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

@inproceedings{Sur2017ImprovedSC,
  title={Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds},
  author={Maitreyi Sur and Tony Suffredini and Stephen M Wessells and Peter H. Bloom and Michael Lanzone and Sheldon Blackshire and Srisarguru Sridhar and Todd Katzner},
  booktitle={PloS one},
  year={2017}
}
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… CONTINUE READING
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