Affiliations: [a] Institute of Information Technology, Vietnam Academy of Science and Technology, Cau Giay, Vietnam
| [b] VNU University of Engineering and Technology, Hanoi City, Vietnam
| [c] Faculty of Technology Education, Hanoi National University of Education, Hanoi City, Vietnam
| [d]
CMR Institute of Technology (Autonomous), Hyderabad, India
| [e] Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi City, Vietnam
Correspondence:
[*]
Corresponding author. Duc-Tan Tran, Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi City 12116, Vietnam. E-mail: [email protected].
Abstract: Modern methods of monitoring help cow farmers save significantly monitoring time and improve cow health care efficiency. Behavioral changes when cows are sick may include increased or decreased daily activities such as increased lying or decreased walking time. Accelerometer advantages are low power consumption, small size, and lightweight. Thus, accelerometers have been widely used to monitor cow behavior. A cow monitoring system usually includes a central processor for receiving and processing information according to a behavioral classification algorithm through the cows’ movements. This paper introduces an effective classification system for Southern Yellow cow behavior using three degrees of freedom (3-DoF) accelerometers. The proposed classifier applied GBDT algorithm (16 seconds window) with five features, offers the good performance while investigating with four Southern Yellow cattle. The classification achievement was assessed and compared to existing ones regarding sensitivity, accuracy, and positive predictive value.
