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

• Topic--Smart Animal Husbandry Key Technologies and Equipment • Previous Articles     Next Articles

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 Published:2022-08-04
  • corresponding author: LIU Jifang E-mail:82101212474@caas.cn;liujifang@caas.cn


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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