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Predictive models of lameness in dairy cows achieve high sensitivity and specificity with force measurements in three dimensions

Published online by Cambridge University Press:  17 August 2015

Jason Dunthorn ,
Robert M Dyer ,
Nagaraj K Neerchal ,
Jonathan S McHenry ,
Parimal G Rajkondawar ,
Gary Steingraber  and
Uri Tasch
Jason Dunthorn
Affiliation:
Department of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA StepAnalysis LLC, 5 Ruby Field Court, Baltimore, MD 21209, USA
Robert M Dyer
Affiliation:
Department of Animal and Food Sciences, College of Agriculture and Natural Resources, University of Delaware, Newark, DE 19717, USA
Nagaraj K Neerchal
Affiliation:
Department of Mathematics and Statistics, University of Maryland at Baltimore County, Baltimore, MD 21250, USA
Jonathan S McHenry
Affiliation:
Department of Mathematics and Statistics, University of Maryland at Baltimore County, Baltimore, MD 21250, USA
Parimal G Rajkondawar
Affiliation:
BouMatic LLC, 1919 South Stoughton Road, Madison, WI 53716, USA
Gary Steingraber
Affiliation:
BouMatic LLC, 1919 South Stoughton Road, Madison, WI 53716, USA
Uri Tasch*
Affiliation:
StepAnalysis LLC, 5 Ruby Field Court, Baltimore, MD 21209, USA
*
*For correspondence; e-mail: tasch@umbc.edu
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Abstract

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.

Keywords

Lamenessdetectionforce plate3 orthogonal dimensions
Type
Research Article
Information
Journal of Dairy Research , Volume 82 , Issue 4 , November 2015 , pp. 391 - 399
Copyright
Copyright © Proprietors of Journal of Dairy Research 2015 

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