| Autor: |
Phi Khanh PC; VNU University of Engineering and Technology, 144 Xuan Thuy, Hanoi City, Vietnam., Tran DT; Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi City, Vietnam., Duong VT; NTT Hi-Tech Institute-Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, Ward 13, District 4, Ho Chi Minh City, Viet Nam., Thinh NH; VNU University of Engineering and Technology, 144 Xuan Thuy, Hanoi City, Vietnam., Tran DN; Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi City, Vietnam. |
| Jazyk: |
angličtina |
| Zdroj: |
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2020 Mar 11; Vol. 17 (4), pp. 2760-2780. |
| DOI: |
10.3934/mbe.2020151 |
| Abstrakt: |
Monitor and classify behavioral activities in cows is a helpful support solution for livestock based on the analysis of data from sensors attached to the animal. Accelerometers are particularly suited for monitoring cow behaviors due to small size, lightweight and high accuracy. Nevertheless, the interpretation of the data collected by such sensors when characterizing the type of behaviors still brings major challenges to developers, related to activity complexity (i.e., certain behaviors contain similar gestures). This paper presents a new design of cows' behavior classifier based on acceleration data and proposed feature set. Analysis of cow acceleration data is used to extract features for classification using machine learning algorithms. We found that with 5 features (mean, standard deviation, root mean square, median, range) and 16-second window of data (1 sample/second), classification of seven cow behaviors (including feeding, lying, standing, lying down, standing up, normal walking, active walking) achieved the overall highest performance. We validated the results with acceleration data from a public source. Performance of our proposed classifier was evaluated and compared to existing ones in terms of the sensitivity, the accuracy, the positive predictive value, and the negative predictive value. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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