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Stoughton, WI, United States

Liu J.,University of Maryland Baltimore County | Dyer R.M.,University of Delaware | Neerchal N.K.,University of Maryland Baltimore County | Tasch U.,University of Maryland Baltimore County | Rajkondawar P.G.,BouMatic LLC
Journal of Dairy Research | Year: 2011

The objective of the study was to evaluate the relationship of veterinary clinical assessments of lameness to probability estimates of lameness predicted from vertical kinetic measures. We hypothesized that algorithm-derived probability estimates of lameness would accurately reflect vertical measures in lame limbs even though vertical changes may not inevitably occur in all lameness. Kinetic data were collected from sound (n=179) and unilaterally lame (n=167) dairy cattle with a 1-dimensional, parallel force plate system that registered vertical ground reaction force signatures of all four limbs as cows freely exited the milking parlour. Locomotion was scored for each hind limb using a 1-5 locomotion score system (1=sound, 5=severely lame). Pain response in the interdigital space was quantified with an algometer and pain response in the claw was quantified with a hoof tester fitted with a pressure gage. Lesions were assigned severity scores (1=minimal pathology to 5=severe pathology). Lameness diminished the magnitude of peak ground reaction forces, average ground reaction forces, Fourier transformed ground reaction forces, stance times and vertical impulses in the lame limbs of unilaterally lame cows. The only effect of lameness on the opposite sound limb was increased magnitude of stance times and vertical impulses in unilaterally lame cows. Symmetry measures of the peak ground reaction forces, average ground reaction forces, Fourier transformed ground reaction forces, stance times and vertical impulses between the left and right hind limbs were also affected in unilateral lameness. Paradoxically, limbs with clinically similar lesion and locomotion scores and pain responses were associated with a broad range of load-transfer off the limb. Substantial unloading and changes in the vertical limb variables occurred in some lameness while minimal unloading and changes in vertical limb variables occurred in other lameness. Corresponding probability estimates of lameness accurately reflected changes in the vertical parameters of limbs and generated low probability estimates of lameness when minimal unloading occurred. Failure to transfer load off limbs with pain reactions, locomotion abnormalities and lesions explained much of the limited sensitivity in lameness detection with vertical limb variables. Copyright © 2011 Proprietors of Journal of Dairy Research. Source


Dunthorn J.,University of Maryland Baltimore County | Dunthorn J.,StepAnalysis LLC | Dyer R.M.,University of Delaware | Neerchal N.K.,University of Maryland Baltimore County | And 4 more authors.
Journal of Dairy Research | Year: 2015

Lameness remains a significant cause of production losses, a growing welfare concern and may be a greater economic burden than clinical mastitis. A growing need for accurate, continuous automated detection systems continues because US prevalence of lameness is 12 5% while individual herds may experience prevalence's of 27 8-50 8%. To that end the first force-plate system restricted to the vertical dimension identified lame cows with 85% specificity and 52% sensitivity. These results lead to the hypothesis that addition of transverse and longitudinal dimensions could improve sensitivity of lameness detection. To address the hypothesis we upgraded the original force plate system to measure ground reaction forces (GRFs) across three directions. GRFs and locomotion scores were generated from randomly selected cows and logistic regression was used to develop a model that characterised relationships of locomotion scores to the GRFs. This preliminary study showed 76 variables across 3 dimensions produced a model with greater than 90% sensitivity, specificity, and area under the receiver operating curve (AUC). The result was a marked improvement on the 52% sensitivity, and 85% specificity previously observed with the 1 dimensional model or the 45% sensitivities reported with visual observations. Validation of model accuracy continues with the goal to finalise accurate automated methods of lameness detection. © Proprietors of Journal of Dairy Research 2015. Source


Dunthorn J.,University of Maryland Baltimore County | Dyer R.M.,University of Delaware | Neerchal N.K.,University of Maryland Baltimore County | McHenry J.S.,University of Maryland Baltimore County | And 3 more authors.
Journal of Dairy Research | Year: 2015

Lameness remains a significant cause of production losses, a growing welfare concern and may be a greater economic burden than clinical mastitis . A growing need for accurate, continuous automated detection systems continues because US prevalence of lameness is 12·5% while individual herds may experience prevalence's of 27·8–50·8%. To that end the first force-plate system restricted to the vertical dimension identified lame cows with 85% specificity and 52% sensitivity . These results lead to the hypothesis that addition of transverse and longitudinal dimensions could improve sensitivity of lameness detection. To address the hypothesis we upgraded the original force plate system to measure ground reaction forces (GRFs) across three directions. GRFs and locomotion scores were generated from randomly selected cows and logistic regression was used to develop a model that characterised relationships of locomotion scores to the GRFs. This preliminary study showed 76 variables across 3 dimensions produced a model with greater than 90% sensitivity, specificity, and area under the receiver operating curve (AUC). The result was a marked improvement on the 52% sensitivity, and 85% specificity previously observed with the 1 dimensional model or the 45% sensitivities reported with visual observations. Validation of model accuracy continues with the goal to finalise accurate automated methods of lameness detection. Copyright © Proprietors of Journal of Dairy Research 2015 Source


Trademark
TECHNOLOGIES HOLDINGS Corporation, Bou Matic Technologies Corporation, Bou Matic Llc, Gascoigne Melotte Holdings Llc and Gascoigne Melotte B.V. | Date: 1993-06-15

milking machines and parts thereof; namely, teat shells, teat liners, milk claws, milk pumps, air separators, sanitary and safety traps and vacuum pump assemblies; high-pressure cleaning machines for use in the livestock industry and in the dairy industry; manure mixing machines and parts thereof; namely, manure separators; power operated cattle feeding machines. electronic apparatuses and parts for animal identification/feed dispensing equipment, electric and electronic equipment and apparatuses for milking machines, electronic milk yield recorders, recorder jars, cattle weighing equipment, end-of-milking indication devices, weighing, measuring, signaling, control and inspection devices and instruments, computers, peripheral computer equipment, computer terminals, computer management systems, microprocessors.

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