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Assessing the effects of sampling frequency on behavioural classification of accelerometer data

Hounslow, J.L., Brewster, L.R., Lear, K.O., Guttridge, T.L., Daly, R., Whitney, N.M. and Gleiss, A.C. (2019) Assessing the effects of sampling frequency on behavioural classification of accelerometer data. Journal of Experimental Marine Biology and Ecology, 512 . pp. 22-30.

Link to Published Version: https://doi.org/10.1016/j.jembe.2018.12.003
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Abstract

Understanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g., diel, tidal, lunar, seasonal, annual) gives unique insight into their ecology. Bio-logging tools such as accelerometers allow the remote study of elusive or inaccessible animals by recording high resolution movement data. Machine learning (ML) is becoming a common tool for automatic classification of behaviours from these types of large data sets. These classifiers often perform best using high sampling frequencies; however, these frequencies also limit archival device recording duration through elevated battery and memory use. In this study we assess the effect of sampling frequency on a ML algorithm's ability to correctly classify behaviours from accelerometer data and present a framework for programming bio-logging devices that maintains classifier performance while optimizing data collection duration. Accelerometer data (30 Hz) were collected from juvenile lemon sharks (Negaprion brevirostris) during semi-captive trials at Bimini, Bahamas, and were ground-truthed to a discrete catalogue of behaviours through direct observation of sharks during trials. The ground-truthed data were re-sampled to a range of sampling frequencies (30, 15, 10, 5, 3 and 1 Hz) and behaviours (swim, rest, burst, chafe, headshake) were classified using a random forest ML algorithm. We demonstrate that as sampling frequency decreases, classifier performance decreases. Best overall classification was achieved at 30 Hz (F-score > 0.790), although 5 Hz was appropriate for classification of swim and rest (F-score > 0.964). For fine-scale behaviours characterised by faster kinematics (headshake, burst and chafe), classification performance was lower across the entire range of sampling frequencies (0.535–0.846, 1–30 Hz), though did not decrease significantly until sampling frequency was <5 Hz. We discuss the effects of signal aliasing and recommend that for best classification of fine-scale behaviours, frequencies >5 Hz are required. However, when seeking to maximise the available device memory and battery capacity and therefore extend deployment duration, 5 Hz is an appropriate sampling frequency for classifying behaviours in similar-sized animals.

Item Type: Journal Article
Publisher: Elsevier BV
Copyright: © 2018 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/43619
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