Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour
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 n...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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Royal Society
2018
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| Online Access: | https://eprints.nottingham.ac.uk/49686/ |
| _version_ | 1848798054989168640 |
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| author | Walton, Emily Casey, Christy Mitsch, Jurgen Vázquez-Diosdado, Jorge A. Yan, Juan Dottorini, Tania Ellis, Keith A. Winterlich, Anthony Kaler, Jasmeet |
| author_facet | Walton, Emily Casey, Christy Mitsch, Jurgen Vázquez-Diosdado, Jorge A. Yan, Juan Dottorini, Tania Ellis, Keith A. Winterlich, Anthony Kaler, Jasmeet |
| author_sort | Walton, Emily |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:13:41Z |
| format | Article |
| id | nottingham-49686 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:13:41Z |
| publishDate | 2018 |
| publisher | Royal Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-496862020-05-04T19:31:26Z https://eprints.nottingham.ac.uk/49686/ 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 Kaler, Jasmeet 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. Royal Society 2018-02-07 Article PeerReviewed 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 sheep behaviour classification algorithm accelerometer and gyroscope sensor signal processing precision livestock monitoring http://rsos.royalsocietypublishing.org/content/5/2/171442 doi:10.1098/rsos.171442 doi:10.1098/rsos.171442 |
| spellingShingle | sheep behaviour classification algorithm accelerometer and gyroscope sensor signal processing precision livestock monitoring Walton, Emily Casey, Christy Mitsch, Jurgen Vázquez-Diosdado, Jorge A. Yan, Juan Dottorini, Tania Ellis, Keith A. Winterlich, Anthony Kaler, Jasmeet Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| title | Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| title_full | Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| title_fullStr | Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| title_full_unstemmed | Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| title_short | Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| title_sort | evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour |
| topic | sheep behaviour classification algorithm accelerometer and gyroscope sensor signal processing precision livestock monitoring |
| url | https://eprints.nottingham.ac.uk/49686/ https://eprints.nottingham.ac.uk/49686/ https://eprints.nottingham.ac.uk/49686/ |