Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning

1. Animal-borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer’s high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine-scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine-scale behaviour. 2. We address this issue by conceptualising fine-scale behaviour in terms of characteristic microevents: impulsive movements producing brief (<1 s) shock signals in accelerometer data. We propose a ‘seek-and-learn’ approach: a novel microevent detection step first locates where shock signals occur (‘seek’) by searching for peaks in envelopes of acceleration data. Robust machine learning (‘learn’) employing m...