基于K-means-BP神经网络的舍饲环境母羊产前运动行为分类识别

为了探讨舍饲圈养方式对怀孕母羊生理及心理健康产生的影响,设计以三轴加速度传感器MPU6050为核心的母羊产前运动行为监测装置,并对37只成年小尾寒羊母羊产前的运动行为(站立、行走、趴卧及刨地)进行分类识别.针对母羊趴卧和站立2种静态行为识别难度大的问题,提出采用K-means聚类算法对趴卧行为进行识别.在此识别结果的基...

Classification and recognition on movement behaviors of ewes in house feeding environment based on K-means and BP neural network

In order to investigate the effects of house feeding on the physiological and mental health of pregnant ewes, a monitoring device of ewe prenatal movement behavior based on three-axis acceleration sensor MPU6050 was designed, and 37 adult Small-Tail Han ewes'' prenatal movement behaviors(standing, walking, lying and digging)were classified and identified. To solve the difficulty in identifying two static behaviors of ewe'' lying and standing behavior, the K-means clustering algorithm was adopted to identify the ewes'' lying behavior. On the basis of the recognition results, the other three kinds of behavior data were corrected, filtered and windowed by MATLAB software, and 19 dimensional feature values of ewes'' movement data were extracted. The principal component analysis method was applied to obtain the six components whose cumulative contribution rate was up to 95%. BP neural network algorithm was then used to identify the other three kinds of movement behaviors(standing, walking and digging). The results showed that the designed ewes'' prenatal movement behavior recognition algorithm could recognize ewes'' prenatal lying behavior with the recognition rate of 99. 2%. The recognition rates of standing, walking and digging were 85. 7%, 88. 9%, and 80. 0%, respectively, which could satisfy the requirements of ewes'' behavior recognition. The recognition rate of the algorithm was improved by 9. 9% after the processing of PCA, and the recognition effect was improved significantly.