Figure 3

Eurasian badger ( Meles meles ) accelerometer traces with sliding-window feature extraction and behavioural classification. For each axis, the blue section represents a 2-s (50 points long) sliding windown (in steps of 0.04 s, 98% overlap), from which features were extracted. The simultaneous decision tree classification for a given set of features is displayed below (see Additional file 4 for video output). The 100 randomly selected behavioural sequences (Additional file 3) were flagged in the continuous stream of accelerometer data as a training data set. Threshold values were then refined through iterative testing on these sequences to obtain the best overall fit. Finally, the selected tree was used to classify the remainder of the data set. g, gravitational acceleration; max., maximum.