The use of kinematics to quantify gait attributes and predict gait scores in dairy cows

Abstract

Detecting walking pattern abnormalities in dairy cows early on holds the potential to reduce the occurrence of clinical lameness. This study aimed to predict gait scores in non-clinically lame dairy cows by using gait attributes based on kinematic data. Markers were placed on 20 anatomical landmarks on 12 dairy cows. The cows were walked multiple times through a corridor while recorded by six cameras, representing 69 passages. Specific gait attributes were computed from the 3D coordinates of the hoof markers. Gait was visually assessed using a 5-point numerical rating system (NRS). Due to the limited number of observations with NRS lower than 2 (n = 1) and higher than 3 (n = 6), the NRS labels were combined into three groups, representing NRS <= 2, NRS = 2.5, and NRS >= 3. The dataset was split into training and testing sets (70:30 ratio), stratified by the distribution of the NRS categories. Random forest (RF), gradient boosting machine (GBM), extreme gradient boosting machine (XGBM), and support vector machine (SVM) with a radial basis kernel models were trained using k-fold repeated cross-validation with hyperparameters defined using a Bayesian optimization. Accuracy, sensitivity, specificity, F1 score, and balanced accuracy were calculated to measure model performance. The GBM model performed best, achieving an overall accuracy and F1 score of 0.65 in the testing set. The findings of this study contribute to the development of an automated monitoring system for early identification of gait abnormalities, thereby enhancing the welfare and longevity of dairy cows.