An exploratory clustering approach for extracting stride parameters from tracking collars on free-ranging wild animals

Full citation: 
Dewhirst, O.P., Roskilly, K., Hubel, T.Y., Jordan, N.R., Golabek, K.A., McNutt, J.W. & Wilson, A.M. (2017). An exploratory clustering approach for extracting stride parameters from tracking collars on free-ranging wild animals. Journal of Experimental Biology doi:10.1242/jeb.146035.
Author/s associated with the CES: 
Neil Jordan

Changes in stride frequency and length with speed are key parameters in animal locomotion research, and are commonly measured on a treadmill. We show that a clustering approach can be used to extract these variables from data collected by a tracking collar, which enables stride parameters to be measured during free-ranging locomotion in natural habitats.

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