An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation
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 thr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/15/1643 |
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| author | Xiaofei Dai Guodong Cheng Lu Yang Yali Wang Zhongkun Li Shuqing Han Jifang Liu |
| author_facet | Xiaofei Dai Guodong Cheng Lu Yang Yali Wang Zhongkun Li Shuqing Han Jifang Liu |
| author_sort | Xiaofei Dai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0e75d80cd87d4904a060eebc22af9905 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-0e75d80cd87d4904a060eebc22af99052025-08-20T03:36:01ZengMDPI AGAgriculture2077-04722025-07-011515164310.3390/agriculture15151643An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical ImplementationXiaofei Dai0Guodong Cheng1Lu Yang2Yali Wang3Zhongkun Li4Shuqing Han5Jifang Liu6Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaThis 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.https://www.mdpi.com/2077-0472/15/15/1643dairy cow lamenessearly lameness detectionimproved InceptionTimefeature fusion |
| spellingShingle | Xiaofei Dai Guodong Cheng Lu Yang Yali Wang Zhongkun Li Shuqing Han Jifang Liu An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation Agriculture dairy cow lameness early lameness detection improved InceptionTime feature fusion |
| title | An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation |
| title_full | An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation |
| title_fullStr | An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation |
| title_full_unstemmed | An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation |
| title_short | An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation |
| title_sort | improved model for online detection of early lameness in dairy cows using wearable sensors towards enhanced efficiency and practical implementation |
| topic | dairy cow lameness early lameness detection improved InceptionTime feature fusion |
| url | https://www.mdpi.com/2077-0472/15/15/1643 |
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