A study of sensor derived features in cattle behaviour classification models

Abstract

Models were developed to classify six different behaviours for a group of seven steers fitted with an accelerometer and pressure sensor. As part of the process, a greedy feature selection method was used to identify the most discriminatory inputs from a diverse set of statistical, spectral and information theory based features. The study showed the second order statistic features (standard deviation and sum of absolute values), which represent the level of motion intensity, were the most discriminatory individual features. The classification performance of models were further enhanced by using spectral features (with statistical features) to capture the periodicity of head movements and to differentiate between the dominant frequencies of various motions. Incorporating feature selection into model development not only improves model performance, but assists in understanding the different motion characteristics that enable behaviours to be discriminated.