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Internet-of- Things Behavior Monitoring System Based on Wearable Inertial Sensors for Classifying Dairy Cattle Health Using Machine Learning

文献类型： 会议论文

第一作者： Zhonghao Shi

作者： Zhonghao Shi 1 ; Zi'Ang Zhang 1 ; Yuanzheng Jia 2 ; Jun Li 1 ; Xinyue Wang 1 ; Yawen Qiu 3 ; Jinfeng Miao 3 ; Fangyuan Chang 4 ; Xiangan Han 2 ; Wei Tang 1 ;

作者机构： 1.Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

2.Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China

3.College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China

4.Department of Design, Shanghai Jiao Tong University, Shanghai, China

关键词： Legged locomotion;Support vector machines;Machine learning algorithms;Inertial sensors;Cows;Real-time systems;Behavioral sciences

会议名称： IEEE International Conference on Artificial Intelligence in Engineering and Technology

主办单位：

页码： 277-282

摘要： In this work, an Internet-of- Things (IoT) system for monitoring dairy cattle behavior is developed using wearable inertial sensors and machine learning algorithms. A behavior recognition model is established using the K-Nearest Neighbors (KNN) algorithm to monitor feeding and movement behaviors, including standing, lying, walking, resting, feeding, and ruminating, with high accuracy above 80%. Based on the recognized behaviors from leg and collar sensors, a health classification model is further developed using the Support Vector Machine (SVM) machine learning algorithm, achieving an accuracy of up to 70%. By embedding these models into the developed IoT system, online real-time monitoring of dairy cattle's daily behaviors and health score is demonstrated, enabling health status alerting. This work lays the foundation for designing IoT systems for real-time monitoring of dairy cattle behaviors and health status classification towards cost-effective livestock farming.

分类号： tp18-53

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