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An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation

文献类型： 外文期刊

作者： Dai, Xiaofei 1 ; Cheng, Guodong 1 ; Yang, Lu 1 ; Wang, Yali 1 ; Li, Zhongkun 1 ; Han, Shuqing 1 ; Liu, Jifang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Blockchain Applicat, Beijing 100081, Peoples R China

关键词： dairy cow lameness; early lameness detection; improved InceptionTime; feature fusion

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 15 期

页码：

收录情况： SCI

摘要： This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a threshold discrimination module using variance of motion-direction acceleration was designed to distinguish states within 2 s, enabling rapid data screening. For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. Typical gait features were fused with deep features automatically learned by the network, enabling accurate discrimination among healthy, mild (early) lameness, and severe lameness. Results showed that the online detection model achieved 80.6% dairy cow health status detection accuracy with 0.8 ms single-decision latency. The recall and F1 score for lameness, including early and severe cases, reached 89.11% and 88.93%, demonstrating potential for early and progressive lameness detection. This study improves lameness detection efficiency and validates the feasibility and practical value of wearable sensor-based gait analysis for dairy cow health management, providing new approaches and technical support for monitoring and early intervention on large-scale farms.

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