moving forward using automated measures for lameness detection education/2010... · moving forward...

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


-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


Measures of weight distribution candetect lameness promptly


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



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)


* 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


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


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


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


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



2 milkings / day

Ste

ps/h

Hour of day

Non-lame

Lame



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


Chapinal et al. 2010. First North American Conference on Precision Dairy Management

Overall gait score

Sym

metr

yo

faccele

rati

on

(%)


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

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