Automated methods for detecting lameness and measuring analgesia in dairy cattle

Chapinal, N.; Passillé, A.M. de; Rushen, J.; Wagner, Sarah

doi:10.3168/jds.2009-2803

Cited by 142 publications

(178 citation statements)

References 21 publications

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“…Also, lame cows (LS4) exhibited much more Activity variability compared with cows with lower LS. This increased variability due to lameness agrees with another study showing that the standard deviation of the weight applied to hind legs decreased in lame (LS >3) cows after lameness relieving ketoprofen injections (Chapinal et al, 2010). In addition, gait variability increased from healthy cows (score 1 on a scale from 1 to 3) to mildly lame cows (score 2) as measured by a pressure mat (Van Nuffel et al, 2013).…”

Section: Discussionsupporting

confidence: 89%

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

Thorup

Munksgaard

Robert

et al. 2015

Animal

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Lameness in dairy herds is traditionally detected by visual inspection, which is time-consuming and subjective. Compared with healthy cows, lame cows often spend longer time lying down, walk less and change behaviour around feeding time.Accelerometers measuring cow leg activity may assist farmers in detecting lame cows. On four commercial farms, accelerometer data were derived from hind leg-mounted accelerometers on 348 Holstein cows, 53 of them during two lactations. The cows were milked twice daily and had no access to pasture. During a lactation, locomotion score (LS) was assessed on average 2.4 times (s.d. 1.3). Based on daily lying duration, standing duration, walking duration, total number of steps, step frequency, motion index (MI, i.e. total acceleration) for lying, standing and walking, eight accelerometer means and their corresponding coefficient of variation (CV) were calculated for each week immediately before an LS. A principal component analysis was performed to evaluate the relationship between the variables. The effects of LS and farm on the principal components (PC) and on the variables were analysed in a mixed model. The first four PC accounted for 27%, 18%, 12% and 10% of the total variation, respectively. PC1 corresponded to Activity variability due to heavy loading by five CV variables related to standing and walking. PC2 corresponded to Activity level due to heavy loading by MI walking, MI standing and walking duration. PC3 corresponded to Recumbency due to heavy loading by four variables related to lying. PC4 corresponded mainly to Stepping due to heavy loading by step frequency. Activity variability at LS4 was significantly higher than at the lower LS levels. Activity level was significantly higher at LS1 than at LS2, which was significantly higher than at LS4. Recumbency was unaffected by LS. Stepping at LS1 and LS2 was significantly higher than at LS3 and LS4. Activity level was significantly lower on farm 3 compared with farms 1 and 2. Stepping was significantly lower on farms 1 and 3 compared with farms 2 and 4. MI standing indicated increased restlessness while standing when cows increased from LS3 to LS4. Lying duration was only increased in lame cows. In conclusion, Activity level differed already between LS1 and LS2, thus detecting early signs of lameness, particularly through contributions from walking duration and MI walking. Lameness detection models including walking duration, MI walking and MI standing seem worthy of further investigation.

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Section: Discussionsupporting

confidence: 89%

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

Thorup

Munksgaard

Robert

et al. 2015

Animal

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“…The best single predictor for lameness, that is, the highest AUC-value, was Θ 2 (AUC = 0.719) in this study. This result is comparable with the single predictor classification performance of the standard deviation of the weight applied to the rear legs (AUC = 0.71) in the study by Chapinal et al (2010). The study by Kamphuis et al (2013) obtained the highest The asymptotic 95% confidence interval tested the null hypothesis that the true area is equal to 0.5.…”

Section: Discussionsupporting

confidence: 74%

“…This result is comparable with the study of Kamphuis et al (2013) who developed a lameness detection model based on sensors monitoring behavioural (activity, milking order) and physiological (live weight) characteristics (AUC = 0.74). The multivariate model in the study by Chapinal et al (2010) obtained, however, a better lameness classification performance (AUC = 0.83). The best single predictor for lameness, that is, the highest AUC-value, was Θ 2 (AUC = 0.719) in this study.…”

Section: Discussionmentioning

confidence: 99%

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Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Hertem

Bahr

Tello

et al. 2016

animal

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The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC) = 0.757 ± 0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate videobased system (AUC = 0.732 ± 0.011) performed better than the multivariate milk sensors (AUC = 0.604 ± 0.026) and the multivariate behaviour sensors (AUC = 0.633 ± 0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC = 0.669 ± 0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ 2 , the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1% ± 4.7%) and specificity (83.2% ± 2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

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“…This aspect can be challenging when trying to recognise behavioural changes, thus sensitive detection methods are required that can notice the subtle changes in behaviour such as changes in locomotion pattern of sows. For example accelerometric devices could be used to detect changes in behaviour as is increasingly the case in cow husbandry (Chapinal et al, 2010a).…”

Section: Discussionmentioning

confidence: 99%

Effect of locomotion score on sows’ performances in a feed reward collection test

Bos

Nalon

Maes

et al. 2015

Animal

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Sows housed in groups have to move through their pen to fulfil their behavioural and physiological needs such as feeding and resting. In addition to causing pain and discomfort, lameness may restrict the ability of sows to fulfil such needs. The aim of our study was to investigate the extent to which the mobility of sows is affected by different degrees of lameness. Mobility was measured as the sow's willingness or capability to cover distances. Feed-restricted hybrid sows with different gait scores were subjected to a feed reward collection test in which they had to walk distances to obtain subsequent rewards. In all, 29 grouphoused sows at similar gestation stage (day 96.6 ± 7 s.d.) were visually recorded for gait and classified as non-lame, mildly lame, moderately lame or severely lame. All sows received 2.6 kg of standard commercial gestation feed per day. The test arena consisted of two feeding locations separated from each other by a Y-shaped middle barrier. Feed rewards were presented at the two feeders in turn, using both light and sound cues to signal the availability of a new feed reward. Sows were individually trained during 5 non-consecutive days for 10 min/day with increasing barrier length (range: 0 to 3.5 m) each day. After training, sows were individually tested once per day on 3 non-consecutive days with the maximum barrier length such that they had to cover 9.3 m to walk from one feeder to the other. The outcome variable was the number of rewards collected in a 15-min time span. Non-lame and mildly lame sows obtained more rewards than moderately lame and severely lame sows ( P < 0.01). However, no significant difference was found between non-lame and mildly lame sows ( P = 0.69), nor between moderately lame and severely lame sows ( P = 1.00). This feed reward collection test indicates that both moderately lame and severely lame sows are limited in their combined ability and willingness to walk, but did not reveal an effect of mild lameness on mobility. These findings suggest that moderately and more severely lame sows, but not mildly lame sows, might suffer from reduced access to valuable resources in group housing systems.

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Automated methods for detecting lameness and measuring analgesia in dairy cattle

Cited by 142 publications

References 21 publications

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Effect of locomotion score on sows’ performances in a feed reward collection test

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