Using Machine Learning for Remote Behaviour Classification—Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs

Using Machine Learning for Remote Behaviour Classification—Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs

Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive chee...

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Bibliographic Details
Main Authors: Lisa Giese, Jörg Melzheimer, Dirk Bockmühl, Bernd Wasiolka, Wanja Rast, Anne Berger, Bettina Wachter
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Sensors
Subjects:
accelerometry
automated behaviour classification
<i>Acinonyx jubatus</i>
cheetah
GPS clusters
supervised machine learning
Online Access:https://www.mdpi.com/1424-8220/21/16/5426

Internet

https://www.mdpi.com/1424-8220/21/16/5426

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