1. Classifiers based on 3D-head acceleration data
Kiel University
Kiel University
Faculty of Agricultural and Nutritional Science
Faculty of Agricultural and Nutritional Science
Institute of Animal Breeding and Husbandry
Institute of Animal Breeding and Husbandry
Classifiers based on 3D-head acceleration data
used for lameness detection in dairy cows
Introduction
Lameness is a great health & welfare problem
Changed behaviour & movement patterns
Decrease in activity level
Compensatory head movement
Material & Methods
Ear tag with a 3D-Acceleration sensor with data frequency of 1 Hz & 10 Hz
Locomotion scoring
3-times a week
1 (normal) to 5 (severely lame)
(SMARTBOW GmbH, Weibern, Austria)
Aim: Determination of classifiers based on 3D-head acceleration data to detect lameness in dairy cows
Classifier calculation for each cow
Lame animals  Hz: 6 lameness of 5 cows
10 Hz: 17 lameness of 10 cows 
Day of detected lameness (locomotion score > 2)
+ 21 days prior
Healthy animals
Equal number of cows 
Matching the lame cows in lactation number & used time period
Learning dataset: Included first 10 days of the dataset
&
Thresholds: Ranges of observed features were extended 
Divided by 250  Threshold-steps (0-250)
Raw
dataset
Learning dataset
& Thresholds
Each day 100 sections
 10 pre-processing methods
median, mean, standard deviation, percentile 25 & 75, skewness, kurtosis, p-variation (p = 1, 2, 3)
 100 values / day & cow
Test dataset:
Included last 12 days, disease blocks were defined
as the day of detected lameness + 3 days prior &
Classifiers:
Application and evaluation of classifiers defined by a pair of pre-processing method & feature
- Alerts were given if the threshold was exceeded
- True positive/negative, false negative/positive events were defined by comparing alerts to disease blocks
Each threshold (0 to 250) corresponds to one point on the ROC (receiver operator characteristics) curve
Pre-processing methods
Test dataset
&
Classifiers
100 sections  daily features 
17 feature calculations
i.e. sum, minimum, maximum, range, skewness
 1 value / day & cow
Features
Results
AUC (area under ROC curve), Sensitivity & Specificity for the three best classifiers
ROC curve for the best classifiers
Pre-processing
Feature
AUC
Sensitivity
Specificity
1 Hz
Percentile 25
Variance
0.90
0.82
Standard deviation
0.83
Range
0.89
0.95
0.79
10 Hz
Kurtosis
Median
0.80
0.71
0.72
Percentile 75
Skewness
0.76
Interquartile range
0.73
1 Hz (AUC=0.90)
Percentile 25 – Variance
10 Hz (AUC=0.80)
Kurtosis – Median
Random choice (AUC=0.50)
Discussion
The 10 Hz dataset had lower AUC values than the 1 Hz dataset
The 1 Hz dataset included less animals, and all lameness were treated according to diagnose  clear lameness
The 10 Hz dataset included cows with multiple detected lameness without further treatment
For further analysis these cows should be excluded or treatment data should be used
Conclusion
Classifiers based on percentile 25, percentile 75, kurtosis & skewness had the best results
Previous studies showed, that these methods indicated changes in activity level
In lame cows acceleration data accumulated around the mean (e.g. higher kurtosis)
Distribution of acceleration data for a lame and a non lame cow
Yvonne Linka ,
Jennifer Salaua,
Susanne Karstenb, Joachim Krietera
aInstitute of Animal Breeding and Husbandry, Christian-Albrechts-University, Kiel, Germany
bTiDa Tier und Daten GmbH, Bosser Str.4c, Westensee, Germany
Funded by:
Stiftung
Schleswig-Holsteinische Landschaft