Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour

Walton, Emily, Casey, Christy, Mitsch, Jurgen, Vázquez-Diosdado, Jorge A., Yan, Juan, Dottorini, Tania, Ellis, Keith A., Winterlich, Anthony and Kaler, Jasmeet (2018) Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour. Royal Society Open Science, 5 (2). p. 171442. ISSN 2054-5703

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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 the effect of all these factors simultaneously. The aim of this study was to evaluate the effects of position (ear and collar), sampling frequency (8, 16 and 32 Hz) of a triaxial accelerometer and gyroscope sensor and window size (3, 5 and 7 s) on the classification of important behaviours in sheep such as lying, standing and walking. Behaviours were classified using a random forest approach with 44 feature characteristics. The best performance for walking, standing and lying classification in sheep (accuracy 95%, F-score 91%–97%) was obtained using combination of 32 Hz, 7 s and 32 Hz, 5 s for both ear and collar sensors, although, results obtained with 16 Hz and 7 s window were comparable with accuracy of 91%–93% and F-score 88%–95%. Energy efficiency was best at a 7 s window. This suggests that sampling at 16 Hz with 7 s window will offer benefits in a real-time behavioural monitoring system for sheep due to reduced energy needs.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/910825
Keywords: sheep behaviour, classification algorithm, accelerometer and gyroscope, sensor, signal processing, precision livestock monitoring
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Veterinary Medicine and Science
Identification Number: https://doi.org/10.1098/rsos.171442
Depositing User: Eprints, Support
Date Deposited: 09 Feb 2018 09:29
Last Modified: 04 May 2020 19:31
URI: https://eprints.nottingham.ac.uk/id/eprint/49686

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