Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ...
The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of th...
| Main Authors: | , , , , , , , |
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| Format: | Dataset |
| Language: | English |
| Published: |
Dryad
2016
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5061/dryad.5rg72 https://datadryad.org/stash/dataset/doi:10.5061/dryad.5rg72 |
| Summary: | The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well-suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging ... : COGUsubset of 1 Common guillemot behavioural data recorded with accelerometer tags.RAZOsubset of 1 Razorbill behavioural data recorded with accelerometer tags. ... |
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