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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
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Gait Phase Recognition of Dairy Cows based on Gaussian Mixture Model and Hidden Markov Model
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 F 1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F 1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F 1 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.
dairy cow lameness / gait phase / gait segmentation / gaussian mixture model / hidden Markov model / Kalman filtering {{custom_keyword}} /
Table 1 GMM-HMM Model parameters表1 GMM-HMM模型参数 |
| 时相 | 初始概率 | 权重 | 均值 | 方差 |
|---|---|---|---|---|
| 静立相 | 0 | 1 | 0.002 | 0.027 |
| 站立相 | 0 | 1 | -0.251 | 3.904 |
| 摆动相 | 1 | 1 | 0.357 | 1976.724 |
Table 2 GMM-HMM recognition results of different gait phases in dairy cows表2 GMM-HMM的奶牛不同步态时相识别结果 |
| 时相 | P/% | R/% | Sp/% | Se/% | F 1 /% |
|---|---|---|---|---|---|
| 静立相 | 89.28 | 90.95 | 93.83 | 90.95 | 90.91 |
| 站立相 | 91.55 | 86.71 | 95.51 | 86.71 | 89.06 |
| 摆动相 | 86.67 | 91.51 | 93.58 | 91.51 | 89.02 |
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Fig. 1 Schematic diagram of measuring siteFig. 2 Sensor wearing method and sensor coordinatesFig. 3 Gait events of dairy cows corresponding to the curve of x-axis acceleration and z-axis angular velocityFig. 4 Sensor 6-axis data line chartFig. 5 Hidden Markov Model structureFig. 6 State transition processof Hidden Markov ModelFig. 7 Flow chart of adaptive recognition method for gait phase of dairy cowsFig. 8 Mathematical characteristics of different gait phases of dairy cows
Table 1 GMM-HMM Model parametersFig. 9 Gait phase recognition results of dairy cowsTable 2 GMM-HMM recognition results of different gait phases in dairy cows/
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