Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

文献类型： 外文期刊

第一作者： Han, Shuqing

作者： Han, Shuqing;Liu, Jifang

作者机构：

关键词： Early lameness detection in dairy cows; Gait reconstruction; Long short-term memory; Autoencoder; Time series anomaly detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 213 卷

页码：

收录情况： SCI

摘要： Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-Autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-Autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learningbased gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-ofview limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

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