Exploration of measurement variation of gait variables for early lameness detection in cattle using the GAITWISE

Nuffel, Annelies Van; Vangeyte, J; Mertens, Kristof; Pluym, Liesbet; Campeneere, Sam De; Saeys, Wouter; Opsomer, Geert; Weyenberg, Stéphanie Van

doi:10.1016/j.livsci.2013.06.013

Cited by 37 publications

(35 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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). Indeed, Activity variability within cow seems to carry important information useful for lameness detection, and calls for further investigation.…”

Section: Discussionsupporting

confidence: 89%

“…Thus, the behavioural time-budget of a dairy cow changes during lameness, making changes in several aspects of activity possible indicators of lameness. Furthermore, lameness may increase within-cow gait variation as measured with a pressure sensitive mat (Van Nuffel et al, 2013) and accelerometer technology (Chapinal et al, 2011). Changes in activity variation are likely to indicate lameness, too.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Thorup

Munksgaard

Robert

et al. 2015

Animal

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

Thorup

Munksgaard

Robert

et al. 2015

Animal

View full text Add to dashboard Cite

show abstract

“…Locomotion score class two had the highest true positive rate, as well as the highest false positive rate. This shows the difficulty in differentiating between mildly lame cows and non-lame cows, which agrees with the findings in previous studies (Van Nuffel et al, 2013;Van Hertem et al, 2014).…”

Section: Discussionsupporting

confidence: 92%

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

View full text Add to dashboard Cite

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.

show abstract

“…The characteristics of impaired locomotion due to lameness in dairy cattle include stride shortening, stiff gait (Flower and Weary, 2006), reduced walking speed (Chapinal et al, 2011;Beer et al, 2016), and decreased foot load and time of weightbearing (Rajkondawar et al, 2006;Van Nuffel et al, 2013;Alsaaod et al, 2017b). Three-dimensional (3D) accelerometers attached to both hind limbs can detect (1) alterations associated with lameness and foot pathologies of the hind limbs (Alsaaod et al, 2017b), and (2) changes in gait while walking on different flooring surfaces (Chapinal et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Locomotion characteristics of dairy cows walking on pasture and the effect of artificial flooring systems on locomotion comfort

Alsaaod

Huber

Beer

et al. 2017

Journal of Dairy Science

View full text Add to dashboard Cite

The locomotion comfort of dairy cows depends on the floor of the walking alleys. Optimal locomotion comfort is given when cows walk on pasture, allowing freedom from discomfort and pain and the expression of normal behavior. This study examined the characteristics of locomotion behavior on pasture (gold standard with optimal locomotion comfort) and compared it with behaviors of cows walking on mastic asphalt or solid rubber mats before and after a routine claw trimming session. Our hypotheses were (1) that gait variables were different on pasture versus mastic asphalt and on mastic asphalt versus rubber, and (2) that claw trimming had an effect on gait variables of cows walking on mastic asphalt. Twenty-four dairy cows kept in a tiestall facility were enrolled in this experimental trial. The pedogram was measured using 2 standalone 3-dimensional accelerometers (400 Hz), attached to the metatarsus of both hind limbs. The extracted pedogram variables included temporal events (kinematic outcome = gait cycle and stance-phase and swing-phase durations) and peaks (kinetic outcome = foot load, toe-off). The cows were further video-recorded to calculate walking speed and stride length. Locomotion score was performed on mastic asphalt to enroll only nonlame cows (locomotion score <3). For comparison between different floor types, repeated-measures ANOVA was performed with the cow as a subject variable, session time of measurement as within-subject variable, and flooring type as a fixed effect. Three separate analyses were performed: pasture versus mastic asphalt (analysis I), solid rubber versus mastic asphalt (analysis II), and the effect of claw trimming on the kinematic and kinetic variables on mastic asphalt (analysis III). All tested gait variables were significantly different between pasture and mastic asphalt floor. The optimal characteristics of locomotion comfort on pasture included shorter duration of gait cycle, longer stance-phase duration, shorter swing-phase duration, higher walking speed, longer stride length, and higher peaks of foot load and toe-off. However, gait variables of cows walking on rubber mats did not show any significant difference compared with the mastic asphalt floor; only stride length tended to be longer on rubber mats. In addition, the stance-and swing-phase durations significantly improved shortly after trimming. The left-right differences of the stanceand swing-phase durations tended to decrease after claw trimming when cows walked on asphalt floor. The results of this study show that solid rubber flooring does not result in significant improvement of the evaluated variables of locomotion comfort compared with mastic asphalt.

show abstract

Exploration of measurement variation of gait variables for early lameness detection in cattle using the GAITWISE

Cited by 37 publications

References 25 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

Locomotion characteristics of dairy cows walking on pasture and the effect of artificial flooring systems on locomotion comfort

Contact Info

Product

Resources

About