This study presents an automated lameness detection system that uses deep-learning image processing techniques to extract multiple locomotion traits associated with lameness. Using the T-LEAP pose estimation model, the motion of nine keypoints was extracted from videos of walking cows. The videos were recorded outdoors, with varying illumination conditions, and T-LEAP extracted 99.6% of correct keypoints. The trajectories of the keypoints were then used to compute six locomotion traits: back posture measurement, head bobbing, tracking distance, stride length, stance duration, and swing duration. The three most important traits were back posture measurement, head bobbing, and tracking distance. For the ground truth, we showed that a thoughtful merging of the scores of the observers could improve intra-observer reliability and agreement. We showed that including multiple locomotion traits improves the classification accuracy from 76.6% with only one trait to 79.9% with the three most important traits and to 80.1% with all six locomotion traits.

翻译：本研究提出了一种自动化跛行检测系统，该系统采用深度学习图像处理技术提取与跛行相关的多类运动特征。通过T-LEAP姿态估计模型，从奶牛行走视频中提取了九个关键点的运动轨迹。视频在户外不同光照条件下采集，T-LEAP模型成功提取了99.6%的正确关键点。利用关键点运动轨迹计算了六项运动特征：背部姿态测量、头部摆动、步距、步长、站立时长和摆动时长。其中最重要的三项特征为背部姿态测量、头部摆动和步距。在基准数据验证方面，本研究表明，合理整合观察者评分可提升评分者内部信度与一致性。实验证明，纳入多类运动特征可将分类准确率从单特征时的76.6%提升至三项关键特征时的79.9%，并进一步达到六项全特征时的80.1%。