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Moving Forward UsingAutomated Measures
for Lameness Detection
Núria Chapinal, PhD
Animal Welfare Program, UBC
April 14, 2010
Introduction
Visual/subjective methods of detection
Automated methods of detection
Examples
Do they work?
Experimental results
Conclusions and practical applications
Outline
Introduction
Lameness is a major welfare and productivityproblem in dairy cattle
Traditional assessment method: visualobservation
Herds are getting larger
Producers have difficulties detecting lame cows
Simple (fast), accurate and repeatable
Introduction
Automated methods of detection available
Automated gait assessment
Automated monitoring of other behaviors
Automated gait assessment
Video motion analysis (Flower et al. 2005)
Ground reaction force (Rajkondawar et al.2006)
Introduction
Lame cows:
Lie down for longer (e.g. Chapinal et al.,2009)
Change weight distribution among legswhen standing (e.g. Rushen et al. 2007;Pastell and Kujala 2007)
Have reduced mobility (e.g. visit a milkingrobot less frequently, Borderas et al. 2008)
Subjective
Vague description of lameness degrees
Inter and intra observer reliability
Not properly validated
Training
Time consuming
Visual methods for gait assessment
Subjective methods for gait assessment
Swinging in/out
Back arch
Joint flexion
Tracking up
Head bob
Asymmetric steps
Reluctance to bear weight
(Flower & Weary 2006 J. Dairy Sci. 89:139-146)
1 = not lame
5 = severelylame
More than 90% of cases correctly classified ashaving a sole ulcer or not.
Gait score can predict sole ulcers
1
1.5
2
2.5
3
3.5
4
-8 -4 0 4
Ov
era
llg
ait
Ove
rall
gait †
** * *
1
1.5
2
2.5
3
3.5
4
-8 -4 0 4
Ov
era
llg
ait
Ove
rall
gait †
** * *
Week relative to diagnosis
Sole ulcer Hemorrhage No lesions
(Chapinal et al. 2009 J. Dairy Sci. 92: 4365-4374)
Swinging in/out
Back arch
Joint flexion
Tracking up
Head bob
Asymmetric steps
Reluctance to bear weight
(Flower & Weary 2006 J. Dairy Sci. 89:139-146)
(Chapinal et al. 2009 J. Dairy Sci. 92: 4365-4374)
Subjective methods for gait assessment
Objective = Repeatable
Reduced labor
Continuous monitoring (changes within cows)= Increased accuracy
Some haven’t been properly validated yet
Becoming affordable
Automated methods for lameness detection
Visits to a milking robot
Activity
Lying behavior (time, bouts)
Steps
Walking acceleration patterns
Weight distribution while standing
Ground reaction force while walking
Automated methods for lameness detection
IceTag accelerometer (IceRobotics)
AfiMilk Pedometer Plus Tag (SAE Afikim)
Hobo G pendant acceleration logger(Onset Computer Corporation)
H-tag motion sensor (SCR)
Activity
Activity
Activity measures
Lying bouts/day
Lying bout duration
Lying time/day
Steps/day
Acceleration patterns
Acceleration patterns
-3.5
-2.5
-1.5
-0.5
0.5
1.5
2.5
3.5
0 1 2 3 4 5
Seconds
Acce
lera
tion
(g)
-3.5
-2.5
-1.5
-0.5
0.5
1.5
2.5
3.5
0 1 2 3 4 5
Seconds
Acc
eler
ati
on
(g)
A
De Passillé et al. J. Dairy Sci. in press
Weight distribution: weighing platform
Weight distribution and shiftingamong legs
FRONT LEFT FRONT RIGHT BACK LEFT BACK RIGHT Total WEIGHT
0
100
200
300
400
500
600
700
10:52:37 10:53:26 10:54:14 10:55:03 10:55:52
Time
Kg
Ground reaction forces: Stepmetrix (BouMatic)
Rajkondawar et al. 2006 J. Dairy Sci. 89:4267-4275
Bicalho et al. 2007 J. Dairy Sci. 90:3294-3300
Lameness scored based on 5 limb movement variables
(measures of stride and weight bearing)
Do they work?
Automated milkingsystems collectdata on cowbehaviour: Lamecows go to roboticmilkers less often
0
20
40
60
80
100
120
Frequent
visitors
Infrequent
visitors%
co
ws
Not lame Lame
Borderas et al. 2008
Can. J. Anim. Sci. 88:1-8
Weight distribution
Lame cows do not distribute weightevenly between contralateral legs
0
100
200
300
400
500
600
700
10:52:37 10:53:26 10:54:14 10:55:03 10:55:52
Time
Kg
BACK LEFT BACK RIGHT TOTAL
Lame cows shift weight more oftenbetween contralateral legs
0
100
200
300
400
500
600
700
10:52:37 10:53:26 10:54:14 10:55:03 10:55:52
Time
Kg
BACK LEFT BACK RIGHT TOTAL
Weight distribution measures
For each pair of legs (front and back)
WEIGHT ASSYMETRY Leg weight ratio = weight on lighter/weight on heavier
leg
E.g. 50% on left leg, 50% on right leg LWR = 50/50 = 1
60% on left leg, 40% on right leg LWR = 40/60 = 0.67
WEIGHT SHIFTING: Variability (SD) over time of weight applied to each pair
of legs
Number of kicks
Weight distribution
Pastell & Kujala 2007 J. Dairy Sci. 90:2283-2292
Not lame Mild lameness
Moderate lameness Severe lameness
Pastell & Kujala 2007 J. Dairy Sci. 90:2283-2292
Measures of weight distribution candetect lameness promptly
Pastell & Kujala 2007 J. Dairy Sci. 90:2283-2292
Combination ofmethods:
Does accuracyincrease?
Experimental set-up for gait scoring andmeasuring weight distribution
WEIGHINGPLATFORM
GAITSCORE
9m
Weight distribution and activity (Exp 1)
Chapinal et al. J. Dairy Sci. in press
Overall gait score correlated with:
• Weight shifting in the rear legs (SD):
r = 0.23 ; P = 0.01
• Symmetry of rear legs (leg weight ratio):
r = - 0.52; P = 0.002
• Frequency of steps:
r = - 0.43; P < 0.001
Weight distribution and activity (Exp 1)
Chapinal et al. J. Dairy Sci. in press
Cows with severe hoof infections were moreasymmetric in the rear legs
• Mean leg weight ratio ± SE =
0.75 ± 0.05 vs. 0.80 ± 0.03; P = 0.006
• OR = 1.2 ; P = 0.03
for each 5% decrease in leg weight ratio
Day 1 Day 2 Day 3 Day 4
LamenessDetection(objective 1)
Effect of analgesia (objective 2)
Ketoprofen (3mg/kg BW) / Saline (im)
Weight distribution and activity (Exp 2)
* Lame cows: overall gait score > 3 (Flower & Weary 2006)
Lameness Detection:Weight distribution among legs
Variable Non-lame Lame OR 95%CI
Rear legs weightvariability (SD, kg)
24.1 ± 2.0 32.6 ± 2.2 * 1.4 1 1.1– 1.8
Front legs weightvariability (SD, kg)
16.5 ± 1.5 22.6 ± 1.7 ** 1.6 1 1.1 – 2.3
Rear legweight ratio
0.9 ± 0.02 0.8 ± 0.02 ** 0.7 2 0.5 – 0.9
1 OR adjusted to a 5-kg increase; 2 OR adjusted to a 5% increase
Chapinal et al. J. Dairy Sci. in press
Lameness Detection:Activity and walking speed
Variable Non-lame Lame OR 95%CI
Lying time(min/day)
720.1 ± 23.2 787.6 ± 27.1 † 1.1 1 1.0 – 1.3
Lying boutduration (min)
73.9 ± 3.9 89.7 ± 4.6 * 1.5 1 1.1 – 2.1
Walking speed(m/s)
1.5 ± 0.4 1.3 ± 0.4 ** 0.7 2 0.5 – 0.9
1 OR adjusted to a 30-min increase; 2OR adjusted to a 0.1 m/s increase
Chapinal et al. J. Dairy Sci. in press
SD of the weight of the rear legs (AUC = 0.71)
SD + lying bout duration (AUC = 0.76)
SD + bout duration + speed (AUC= 0.83)
Combining measures of weight distribution,activity and walking speed improved lameness
detection
Chapinal et al. J. Dairy Sci. in press
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
1 - Specificity
Se
ns
itiv
ity
The SD of the weight applied to the rear legssignificantly decreased after the ketoprofen
injections
15
20
25
30
35
40
1 2 3 4
Day
SD
of
the
weig
ht
(kg
)
InjectionsKetoprofen
Saline
Chapinal et al. J. Dairy Sci. in press
Lame cows show:
Asymmetry in weight distribution
Frequent weight transfer
Lame cows usually have
Longer lying bouts
Longer daily lying times
Decreased activity (steps)
although differences not alwayssignificant!
Lameness Detection
Variability in activity measures
Lyin
gti
me
(h/d
)
Ito et al. 2009 J. Dairy Sci. 92:4412-4420
Farm ID
Lyin
gti
me
(h/d
ay)
0
20
40
60
80
100
120
140
160
180
200
0 2 4 6 8 10 12 14 16 18 20 22
Hour of day
Ste
ps
/h
Variability in activity measures
Chapinal et al. J. Dairy Sci. in press
2 milkings / day
Ste
ps/h
Hour of day
Non-lame
Lame
Variability in activity measures
Chapinal et al. J. Dairy Sci. in press
0
20
40
60
80
100
120
140
160
180
200
0 2 4 6 8 10 12 14 16 18 20 22
Hour of day
Ste
ps
/h
3 milkings / day
Ste
ps/h
Hour of day
Non-lame
Lame
Acceleration patterns
Chapinal et al. 2010. First North American Conference on Precision Dairy Management
Overall gait score
Sym
metr
yo
faccele
rati
on
(%)
Acceleration patterns
Chapinal et al. 2010. First North American Conference on Precision Dairy Management
Automated methods of weight distribution andactivity show promise for on-farm lamenessdetection, particularly when combined
These methods may provide a tool for futureevaluation of lameness therapies
Conclusions
Continuous monitoring of activity
(heat detection, lameness,
other diseases)
Milking robots (+ weighing platform?)
Practical applications
Borderas, T.F., A. R. Fournier, J. Rushen, and A.M. de Passillé. 2008. Effect oflameness on dairy cows' visits to automatic milking systems. Can. J. Ani. Sci. 88:1-8.
Bicalho, R. C., S. H. Cheong, G. Cramer, and C. L. Guard. 2007. Associationbetween a visual and an automated locomotion score in lactating Holstein cows. J.Dairy Sci. 90:3294-3300.
Chapinal, N., A. M. de Passille, and J. Rushen. 2009. Weight distribution and gait indairy cattle are affected by milking and late pregnancy. J. Dairy Sci. 92:581-588.
Chapinal, N., A. M. de Passillé, J. Rushen, and S. Wagner. Automated methods forthe detection of lameness and analgesia in dairy cattle. J. Dairy Sci. (in press).
Chapinal, N., A. M. de Passillé, J. Rushen, and S. Wagner. Effect of hoof trimmingon gait, weight distribution and activity of dairy cattle. J. Dairy Sci. (in press).
Chapinal, N., M. Pastell, L. Hänninen, J. Rushen, A.M. de Passillé. 2010. Walkingacceleration patters as a method for lameness detection. Proceedings of the FirstNorth American Conference on Precision Dairy Management, p.126-127.
References
De Passillé, A. M., M. B. Jensen, N. Chapinal, and J. Rushen. Technical note: Useof accelerometers to describe gait patterns in dairy calves. J. Dairy Sci. (in press).
Flower, F. C., D. J. Sanderson, and D. M. Weary. 2005. Hoof pathologies influencekinematic measures of dairy cow gait. J. Dairy Sci. 88:3166-3173.
Flower, F. C. and D. M. Weary. 2006. Effect of hoof pathologies on subjectiveassessments of dairy cow gait. J. Dairy Sci. 89:139-146.
Ito, K., D. M. Weary, and M. A. G. von Keyserlingk. 2009. Lying behavior: Assessingwithin- and between-herd variation in free-stall-housed dairy cows. J. Dairy Sci. 92:4412-4420.
Rushen, J., E. Pombourcq, and A. M. d. Passillé. 2007. Validation of two measuresof lameness in dairy cows. Appl. Anim. Behav. Sci. 106:173-177.
Pastell, M. E. and M. Kujala. 2007. A probabilistic neural network model forlameness detection. J. Dairy Sci. 90:2283-2292.
Rajkondawar, P. G., M. Liu, R. M. Dyer, N. K. Neerchal, U. Tasch, A. M. Lefcourt, B.Erez, and M. A. Varner. 2006. Comparison of models to identify lame cows based ongait and lesion scores, and limb movement variables. J. Dairy Sci. 89:4267-4275.
References