High accuracy at low frequency: detailed behavioural classification from accelerometer data [RESEARCH ARTICLE]

Jack Tatler, Phillip Cassey, and Thomas A. A. Prowse Accelerometers are a valuable tool for studying animal behaviour and physiology where direct observation is unfeasible. However, giving biological meaning to multivariate acceleration data is challenging. Here, we describe a method that reliably classifies a large number of behaviours using tri-axial accelerometer data collected at the low sampling frequency of 1 Hz, using the dingo (Canis dingo) as an example. We used out-of-sample validation to compare the predictive performance of four commonly used classification models (Random Forest, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes). We tested the importance of predictor variable selection and moving window size for the classification of each behaviour and overall model performance. Random Forests produced the highest out-of-sample classification accuracy, with our best performing model predicting 14 behaviours with a mean accuracy of 87%. We also investigated the relationship between overall dynamic body acceleration (ODBA) and the activity level of each behaviour given its increasing use in ecophysiology as a proxy for energy expenditure. ODBA values for our four ‘high activity’ behaviours were significantly greater than all other behaviours, with an overall positive trend between ODBA and intensity of movement. We show that a Random Forest model of relatively low complexity can mitigate some major challenges associated with establish...

Source: Journal of Experimental Biology - October 15, 2018 Category: Biology Authors: Tatler, J., Cassey, P., Prowse, T. A. A. Tags: RESEARCH ARTICLE Source Type: research