Data from: Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour
Walton, Emily et al. (2018), Data from: Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour, Dryad, Dataset, https://doi.org/10.5061/dryad.h5c80
Automated behavioural classification and identification through sensors has the potential to improve health and welfare of the animals. Position of a sensor, sampling frequency and window size of segmented signal data has a major impact on classification accuracy in activity recognition and energy needs for the sensor, yet, there are no studies in precision livestock farming that have evaluated effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8Hz, 16Hz and 32 Hz) of a tri-axial accelerometer and gyroscope sensor and window size (3s, 5s and 7s) of on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with forty-four feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F-score 91-97%) was obtained using combination of 32Hz, 7s and 32Hz, 5s for both ear and collar sensors, although, results obtained with 16Hz and 7s window were comparable with accuracy of 91-93% and F-score 88-95%. Energy efficiency was best at a 7s window. This suggests that sampling at 16Hz with 7s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs.