Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data...

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Main Authors: Gian Beer, Maher Alsaaod, Alexander Starke, Gertraud Schuepbach-Regula, Hendrik Müller, Philipp Kohler, Adrian Steiner
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4871330?pdf=render
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author Gian Beer
Maher Alsaaod
Alexander Starke
Gertraud Schuepbach-Regula
Hendrik Müller
Philipp Kohler
Adrian Steiner
author_facet Gian Beer
Maher Alsaaod
Alexander Starke
Gertraud Schuepbach-Regula
Hendrik Müller
Philipp Kohler
Adrian Steiner
author_sort Gian Beer
collection DOAJ
description This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.
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spelling doaj.art-6a3cbc3c5ca64d26a1b28514c7ab183d2022-12-22T03:30:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01115e015579610.1371/journal.pone.0155796Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.Gian BeerMaher AlsaaodAlexander StarkeGertraud Schuepbach-RegulaHendrik MüllerPhilipp KohlerAdrian SteinerThis study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.http://europepmc.org/articles/PMC4871330?pdf=render
spellingShingle Gian Beer
Maher Alsaaod
Alexander Starke
Gertraud Schuepbach-Regula
Hendrik Müller
Philipp Kohler
Adrian Steiner
Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.
PLoS ONE
title Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.
title_full Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.
title_fullStr Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.
title_full_unstemmed Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.
title_short Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.
title_sort use of extended characteristics of locomotion and feeding behavior for automated identification of lame dairy cows
url http://europepmc.org/articles/PMC4871330?pdf=render
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