Due to its detrimental effect on cow welfare, health and production, lameness in dairy cows has received quite a lot of attention in the last few decades—not only in terms of prevention and treatment of lameness but also in terms of detection, as early treatment might decrease the number of severely lame cows in the herds as well as decrease the direct and indirect costs associated with lameness cases. Generally, lame cows are detected by the herdsman, hoof trimmer or veterinarian based on abnormal locomotion, abnormal behavior or the presence of hoof lesions during routine trimming. In the scientific literature, several guidelines are proposed to detect lame cows based on visual interpretation of the locomotion of individual cows (i.e., locomotion scoring systems). Researchers and the industry have focused on automating such observations to support the farmer in finding the lame cows in their herds, but until now, such automated systems have rarely been used in commercial herds. This review starts with the description of normal locomotion of cows in order to define ‘abnormal’ locomotion caused by lameness. Cow locomotion (gait and posture) and behavioral features that change when a cow becomes lame are described and linked to the existing visual scoring systems. In addition, the lack of information of normal cow gait and a clear description of ‘abnormal’ gait are discussed. Finally, the different set-ups used during locomotion scoring and their influence on the resulting locomotion scores are evaluated.

Despite the research on opportunities to automatically measure lameness in cattle, lameness detection systems are not widely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat them properly and in a timely fashion. Many papers have focused on the automated measurement of gait or behavioral cow characteristics related to lameness. In order for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cows need to be developed and validated. Few studies have reached this latter stage of the development process. Also, comparison between the different approaches is impeded by the wide range of practical settings used to measure the gait or behavioral characteristic (e.g., measurements during normal farming routine or during experiments; cows guided or walking at their own speed) and by the different definitions of lame cows. In the majority of the publications, mildly lame cows are included in the non-lame cow group, which limits the possibility of also detecting early lameness cases. In this review, studies that used sensor technology to measure changes in gait or behavior of cows related to lameness are discussed together with practical considerations when conducting lameness research. In addition, other prerequisites for any lameness detection system on farms (e.g., need for early detection, real-time measurements) are discussed.

As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic lameness detection. A sensor attached to the cow was preferred, followed by a walkover system and a camera system. In general, visual lameness detection was preferred over automatic detection systems, but this preference changed after informing farmers about the consequences of lameness. To conclude, the system cost and performance were important features, but dairy farmers should be sensitized on the consequences of lameness and its effect on farm profitability.

Increasing lameness problems associated with intensified dairy cattle production has lead to the development of several techniques to automatically detect these problems. Comparisons of these new measuring techniques of cow locomotion with the conventional subjective observer scoring are scarce. In order to better understand human observers’ gait scoring, cows walking on a pressure-sensitive mat were evaluated for kinematic gait variables and a visual assessment of gait was also made via video recording. Forty of these videos were used for subjective gait scoring on a 3-point scale, and the observers were also asked to report any observed abnormalities (lameness indicators) that had influenced their scoring. Relationships between reported lameness indicators and subjective gait scores, between subjective gait scores and measured kinematic variables of cow locomotion and between reported lameness indicators and measured kinematic variables of cow locomotion were investigated. In general, observers based their gait score on reported indicators such as ‘tenderness’, ‘arched back’, ‘irregular gait’ and ‘increased abduction’. All of these four reported lameness indicators were correlated with measured kinematic ‘variables of asymmetry’, ‘stance time’ or both, suggesting that human observers are capable of detecting changes within these lameness indicators as measured by the pressure-sensitive mat. ‘Increased abduction’ appeared harder to detect and was reported more frequently by observers already experienced with gait scoring. Also, the measured kinematic variables of ‘stance time’ and ‘measures of asymmetry between left and right limbs’ as measured by the pressure-sensitive mat, show potential in predicting the gait score given. These reported lameness indicators and measured kinematic variables —mutually correlated and both related to the gait scores — were considered promising for subjective gait scoring in general.