Accuracy of Detecting Degrees of Lameness in Individual Dairy Cattle Within a Herd Using Single and Multiple Changes in Behavior and Gait.

Lameness adversely affects the welfare and productivity of dairy cows. This study quantifies and analyzes key gait characteristics of cows with varying locomotion scores, evaluating their effectiveness for lameness detection in computer vision systems while considering individual specificity. Six key characteristics-back arch, head bob, speed, step overlap, supporting phase, and hoof step time-were analyzed to assess their distribution across different locomotion scores. Through a comparative analysis of single-parameter and multiple-parameter classification models, we quantitatively demonstrated that models using multiple characteristics significantly outperformed single-parameter models, achieving an accuracy of 84% and a Macro-F1 score of 0.81, while better accounting for individual variability. Among the characteristics, step overlap, supporting phase, and back arch showed higher relative importance in the classifiers. Back arch was a strong indicator of severe lameness, while step overlap and supporting phase were more effective for detecting mild cases. A hierarchical classification approach further improved performance by minimizing the impact of less relevant characteristics. This study highlights the importance of integrating multiple gait and posture features for robust lameness detection, providing practical insights for automated systems.