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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 53-63.doi: 10.12133/j.smartag.SA202204003

• 专题——智慧畜牧关键技术与装备 • 上一篇    下一篇

基于高斯混合-隐马尔科夫融合算法识别奶牛步态时相

张楷(), 韩书庆, 程国栋, 吴赛赛, 刘继芳()   

  1. 中国农业科学院农业信息研究所/农业农村部区块链农业应用重点实验室，北京 100081
  • 收稿日期:2022-04-08 出版日期:2022-06-30
  • 基金资助:
    国家自然科学基金项目(32102600);中国农业科学院科技创新工程项目(CAAS-ASTIP-2016-AII);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2021-33)
  • 作者简介:张 楷（1997－），男，硕士研究生，研究方向为畜禽行为识别与畜牧物联网技术。E-mail：82101212474@caas.cn
  • 通信作者: 刘继芳（1965－），男，博士，研究员，研究方向为农业信息管理。E-mail：liujifang@caas.cn

Gait Phase Recognition of Dairy Cows based on Gaussian Mixture Model and Hidden Markov Model

ZHANG Kai(), HAN Shuqing, CHENG Guodong, WU Saisai, LIU Jifang()   

  1. Agricultural Information Institute of Chinese Academy of Agricultural Sciences/ Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
  • Received:2022-04-08 Online:2022-06-30
  • corresponding author: LIU Jifang, E-mail：liujifang@caas.cn
  • About author:ZHANG Kai, E-mail：82101212474@caas.cn
  • Supported by:
    National Natural Science Foundation of China(32102600);Chinese Academy of Agricultural Sciences Innovation Project(CAAS-ASTIP-2016-AII);Special Fund for Basic Scientific Research Business of Central-level Public Welfare Scientific Research Institutes (JBYW-AII-2021-33)

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摘要：

奶牛步态时相是反映奶牛健康及跛行严重程度的重要指标。为准确自动识别奶牛步态时相，本研究提出一种融合高斯混合模型 （Gaussian Mixture Model，GMM） 和隐马尔科夫模型 （Hidden Markov Model，HMM）的无监督学习奶牛步态时相识别算法 GMM-HMM。使用惯性测量单元采集奶牛后肢加速度和角速度信号，通过卡尔曼滤波消除噪声，筛选并提取特征值，构建GMM-HMM模型，实现奶牛静立相、连续步态中的站立相和摆动相等3种步态时相的自动识别。结果表明，静立相识别的准确率、召回率和F1分别为89.28%、90.95%和90.91%，连续步态中的站立相识别的准确率、召回率和F1分别为91.55%、86.71%和89.06%，连续步态中的摆动相识别的准确率、召回率和F1分别为86.67%、91.51%和89.03%。奶牛步态分割的准确率为91.67%，相较于基于事件的峰值检测法和动态时间规整算法准确率分别提高了4.23%和1.1%。本研究可为下一步基于穿戴式步态分析的奶牛跛行特征提取提供技术参考。

关键词: 奶牛跛行, 步态时相, 步态分割, 高斯混合模型, 隐马尔科夫模型, 卡尔曼滤波

Abstract:

The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features.

Key words: dairy cow lameness, gait phase, gait segmentation, gaussian mixture model, hidden Markov model, Kalman filtering

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