可穿戴设备部署位置对羊只行为识别的影响与分析

Influence and analysis of deployment location of wearable devices on sheep behavior recognition

羊只的行为状态能够直接反映其健康状况及所处的生理阶段,为实现自动化的羊只行为识别,以圈养的小尾寒羊为试验对象,构建一个基于加速度传感器的可穿戴式行为监测装置。利用MPU9250为核心的九轴姿态传感器采集羊只静止、行走和进食的行为信息,并将传感器分别部署在羊只颈部、背部靠近前腿处、前腿和后腿四个不同的位置。对于采集的数据,利用去噪和降维进行预处理,并分别利用k-means和SVM进行分类识别。k-means均值聚类算法对颈部处的行为识别准确率最高,为79.34%,SVM支持向量机算法对颈部处的行为识别准确率最高,为92.63%。SVM算法用于羊只行为识别的整体准确率高于k-means,且传感器部署在羊只颈部时在不同识别方法下,识别效率均优于其他部位。研究结果对于构建自动化的羊只行为检测系统具有重要的现实意义。

The behavior state of sheep can directly reflect its health status and physiological stage. In order to realize automatic behavior recognition of sheep, the paper constructed a wearable behavior monitoring device based on acceleration sensor, taking captive small tail Han sheep as the experimental object. The nine-axis posture sensor with MPU9250 as the core was used to collect the behavior information of the sheep when they were still, walking and eating, and the sensors were deployed in the neck, back near the front leg, front leg and back leg respectively. For the collected data, noise reduction and dimension reduction were used for preprocessing, and k-means and SVM were used for classification and recognition respectively. The k-means clustering algorithm has the highest accuracy rate of 79.34% for cervical behavior recognition, and the SVM support vector machine algorithm had the highest accuracy rate of 92.63% for cervical behavior recognition. The experimental results showed that the overall accuracy of SVM method for sheep behavior recognition was higher than that of k-means, and the recognition efficiency of sensors located in sheep neck was better than other parts under different recognition methods. The results of this study have important practical significance for the construction of automatic sheep behavior detection system.