The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Chimienti, MariannaCornulier, ThomasOwen, EllieBolton, MarkDavies, Ian M.Travis, Justin M. J.Scott, Beth E.2016-02-092016-02-092016-02Chimienti, M, Cornulier, T, Owen, E, Bolton, M, Davies, I M, Travis, J M J & Scott, B E 2016, 'The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data', Ecology and Evolution, vol. 6, no. 3, pp. 727–741. https://doi.org/10.1002/ece3.19142045-7758ORCID: /0000-0001-5412-3952/work/162251681http://hdl.handle.net/2164/5503Acknowledgments This project and the tags deployed on both seabird's species were fund by NERC (grant number NE/K007440/1), Marine Scotland Science and Seabird Tracking and Research (STAR) Project led by the Royal Society for the Protection of Birds (RSPB). We would like to thank Rob Hughes, Tessa Cole and Ruth Brown for helping in the data collection, the Bird Observatory of Fair Isle for supporting the fieldwork and the Marine Collaboration Research Forum (MarCRF).152375787engAccelerometer dataanimal movementsbehavioral classiﬁcationunsupervised learningQH301 BiologyNatural Environment Research Council (NERC)NE/K007440/1QH301The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer dataJournal article10.1002/ece3.191463