Natasa, K, Hussain, A, Khan, W, Sneddon, J and Mason, A (2020) Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data. In: Intelligent Computing Methodologies , 12465. pp. 408-419. (ICIC 2020, 2-5th October 2020, Bari, Italy).

Preview

Text 234_FSP.pdf - Accepted Version Download (767kB) | Preview

Abstract

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	This is a post-peer-review, pre-copyedit version of an article published in Intelligent Computing Methodologies. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-60796-8_35
Uncontrolled Keywords:	Accelerometer data Animal activity recognition Feature extraction Machine learning Random forest Sheep behaviour Signal processing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software S Agriculture > S Agriculture (General) S Agriculture > SF Animal culture
Divisions:	Biological & Environmental Sciences (from Sep 19) Computer Science & Mathematics
Publisher:	Springer
Date Deposited:	26 Oct 2020 11:50
Last Modified:	13 Apr 2022 15:18
DOI or ID number:	10.1007/978-3-030-60796-8_35
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13910

View Item