Development of an automated detection system for lameness in cattle: The GAITWISE system

As a result of the growth in dairy production, dairy farms have intensified with more cattle on fewer farms and higher productivity per animal and per caretaker. Consequently, farmers have less time to observe and monitor cows and technology is being used to support the farmers in their management, especially by monitoring the cows health.Besides reduced fertility and mastitis, lameness is one of the top most costly health problems in dairy cows. Unfortunately, not only its effect on farm profitability (caused by drug treatment, veterinary costs, reduced milk production, reduced reproductive performance and shorter life expectancy) is underestimated, but also its detrimental effect on cow health and welfare. With a lameness prevalence of up to 72 %, the levels in European dairy herds are unacceptably high and hence, minimizing the occurrence is one of the greatest challenges the dairy industry is currently facing. To apply proper treatment farmers must be able to detect their lame cows in the herd in an early stage. Therefore, the general aim of this PhD is to develop an automatic system for lameness detection to help farmers in better detecting lame cows in their herd. To achieve this, a walk-over device - called Gaitwise - with an integrated pressure sensitive mat and specific software was developed. Gaitwise measures spatial (e.g. step length), temporal (e.g. stance time) and force related gait variables of claw-floor interactions of cows walking over the measurement zone. Assuming that these gait variables change when a cow develops lameness, Gaitwise could serve as a lameness detection system that alerts the farmer of cows that show abnormal changes in these variables that are related to lameness. In order for this system to be used in daily practice, measurements were fully automated and gait variables are available in real time. In a next step, two groups of gait variables are calculated: the basic gait variables describing the basic gait of cows and ten more specific gait variables that are often used for locomotion scoring in lameness research ( Stride length , stride time , stance time , step overlap , abduction and variables covering asymmetry between left and right limbs in step width, step length, step time, stance time and force). Relevant associations between these measured specific gait variables and corresponding lameness attributes scored by an observer were found. This suggests that the measured variables are functionally relevant for lameness research and detection. Also, a test-retest study revealed that the measurements of the cows walking on the measurement zone are highly reliable. This makes the Gaitwise system a good instrument for gait analysis in the context of lameness detection, because the changes in gait variables due to lameness are likely to be attributed to lameness and not to cow variability. In order to select the specific variables that were most suited for a detection model, the specific gait variables were compared to a reference method for lameness detection usingobserver locomotion scoring of the cows. All the tested variables were different between groups of non-lame, mildly lame and severely lame cows. Variables of asymmetry in step length , asymmetry in stance time , asymmetry in step time and stance time , step overlap and abduction seemed to have the highest potential for the detection of lameness in cattle. The lameness detection model that was built based on these results, showed promising overall sensitivity and specificity, especially in detecting severely lame cows. However, the results of this validation of the detection model revealed that detecting mildly lame cows is the most challenging as the differences with non-lame cows are much smaller. The added value of lameness detection systems would increase considerably when - besides severely lame cows which are more easily spotted by the farmers - also the mildly lame cows could be detected. To improve the detection of mildly lame cases, two approaches were investigated: (1) the potential of other gait variables for detecting lameness in such early stages was investigated and (2) the normal variation of the specific gait variables caused by cow (age, lactation stage, etc.) or environmental (dark environment, wet flooring)) factors was evaluated, because this might cause misclassification of cows and hence hamper the success rate of the detection system. The first approach is based on the fact that in human gait research, increased stride-to-stride fluctuations (i.e. gait inconsistency) were found to be more closely related to early health problems compared to average gait variables. Therefore, the potential of gait inconsistency variables was investigated for early lameness detection in cows. In other words, will a cow that develops lameness first alter its gait by occasionally taking a shorter stride before altering its gait to shorter strides in general? Using two case-control studies, both the basic gait variables and the new gait inconsistency variables were compared between non-lame and severely lame cows and non-lame and mildly lame cows). The inconsistency gait variables were able to show significant differences between non-lame and mildly lame cows, where the more basic gait variables could not. In addition, this data set was used to build a lameness detection model using solely the basic variables and a second model using both the basic and the inconsistency variabels. The second model using the inconsistency variables outperformed by far the model based on only basic gait variables. It was therefore concluded that these new variables of inconsistent gait are promising for assessing lameness at an early stage. In addition, they might even aid in detecting the location of the lameness problem, i.e. which leg is lame. Whether these inconsistency variables are more promising in detecting lameness at an early stage compared to basic or specific gait variables should be investigated in future research using individual detection models. Finally, as cow (e.g. age, gestation stage) or environmental (e.g. wet flooring) factors can also influence cow gait, such changes in gait that are not related to lameness can cause misclassification of cows and hence hamper the success rate of the detection system. Therefore, in the second approach, this normal variation of specific gait variables was investigated in a pilot study. The results show significant influences of wet surfaces, age, lactation and gestation stage on the gait variables. Hence, this normal variation within cows together with the specific way of walking of individual cows should be taken into account when developing a lameness detection algorithm. An individual cow model might therefore outperform the models using group thresholds resulting in better detection of mildly lame cows. This special emphasis on detection of the mildly lame cows will provide more added value for the farmers compared to detecting the severely lame cows.Based on the results obtained in this PhD research, some short-term future work to further improve the Gaitwise system as a lameness detection tool has been suggested. First, future work should focus on pointing out the benefits (less financial losses) to the farmers and decrease the system s production costs. Therefore, the cost-benefit of a lameness detection system should be analysed, the farmers expectations of such lameness detection system should be gathered and more awareness for the effect of lameness on their farm profit and on the health and welfare of their herd should be created. Also possible ways to downscale Gaitwise in order to make it less expensive should be investigated. In addition, several approaches should be tested to further improve the detection level of Gaitwise. The inconsistency variables, adjusted specific variables or other new variables could be tested for their accuracy to detect mildly lame cows. The detection algorithm could be improved by setting individual thresholds instead of group thresholds, by taking the normal variation of gait variables during gestation or lactation into account, and by combining Gaitwise data with data already available on farm and data of other sensortechnologies (accelerometers, StepMetrixTM, etc.).nrpages: 209status: publishe