The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.

Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions on an Israeli dairy farm, using a 3Dcamera. A live on-the-spot assessed 5-point locomotion score was the reference for the automatic lameness score evaluation. A dataset of 186 cows with four automatic lameness scores and four live locomotion score repetitions was used for testing three different classification methods. The analysis of the automatic scores as independent observations led to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on four individual consecutive measures obtained a correct classification rate of 60.2%. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 90.9% was obtained. Strict binary classification to Lame vs. Not-Lame categories reached 81.2% correct classification rate. The use of cow individual consecutive measurements improved the correct classification rate of an automatic lameness detection system.

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Currently, diagnosis of lameness at an early stage in dairy cows relies on visual observation by the farmer, which is time consuming and often omitted. Many studies have tried to develop automatic cow lameness detection systems. However, those studies apply thresholds to the whole population to detect whether or not an individual cow is lame. Therefore, the objective of this study was to develop and test an individualized version of the body movement pattern score, which uses back posture to classify lameness into 3 classes, and to compare both the population and the individual approach under farm conditions. In a data set of 223 videos from 90 cows, 76% of cows were correctly classified, with an 83% true positive rate and 22% false positive rate when using the population approach. A new data set, containing 105 videos of 8 cows that had moved through all 3 lameness classes, was used for an ANOVA on the 3 different classes, showing that body movement pattern scores differed significantly among cows. Moreover, the classification accuracy and the true positive rate increased by 10 percentage units up to 91%, and the false positive rate decreased by 4 percentage units down to 6% when based on an individual threshold compared with a population threshold.

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