USAGE OF TRI-AXIAL ACCELERATION OF THE
HIND LEG FOR RECOGNIZING SHEEP BEHAVIOR
- Miroslav Radeski, Vlatko IlieskiAnimal Welfare Center, Faculty of Veterinary Medicine,
University “Ss. Cyril and Methodius”,
Skopje, R. Macedonia
Varieties of hind leg position and locomotion:
POSTURE
LAYING
STANDING
Varieties of hind leg position and locomotion:
GAIT TYPES
WALKING
RUNNING
TROTTING
Tri-axial accelerometers – Operating principles
Axis acceleration equation:
x=cos(180-x)
Z-axis (g)
F
O
R
C
E
Sum vector acceleration:
Sum vector=
𝒙𝟐 + 𝒚𝟐 + 𝒛𝟐
Y-axis (g)
X-axis (g)
Accel
erom
eter
x=1g; y=0
x=-1g; y=0
Accelerometer
x=0g; y=1
x=0g; y=-1
Attaching accelerometer on the animal:
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
1,023
1,000
1,003
1,003
0,990
1,003
1,003
1,023
1,003
0 993
1,003
1,003
0,990
0,993
1,000
0,993
0,993
1,000
1,003
1,011
1,003
1,000
0,981
0,978
1,023
1,017
1,051
1,023
0,992
0,992
0,996
0,970
0,925
0,948
0,984
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,425
0,425
0,400
0,425
0,425
0,400
0,400
0,425
0,400
0,400
0,425
0,425
0,450
0,425
0,425
0,425
0,425
0,475
0,450
0,475
0,425
0,450
0,450
0,400
0,450
0,400
0,400
0,450
-0,100
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,125
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,125
-0,100
-0,100
-0,150
-0,150
-0,100
-0,125
-0,125
-0,150
-0,125
-0,125
-0,125
-0,025
Objectives
 Interpretation of gathered data from the accelerometer
attached on the hind leg of sheep
 Optimized method for discrimination of:
• Posture (standing & lying)
• Gait type (walking, trotting & running)
 Stride models and kinematic parameters for walking &
running
Animals & Accelerometers
 Number of animals:
• Standing – 6 sheep (3 ewes & 3 rams)
• Gait – 6 sheep (3 ewes & 3 rams)
• Lying – 7 sheep
Z
Y
 Accelerometer
X
• HOBO Pendant G tri-axial acceleration data logger
• Logger settings:
- recordings of x, y, z axes
- logging interval - fast mode
0.03s(33Hz) ≈ 100 readings/3 seconds (max. duration of 10 min.)
Video & Software
 Video recording:
SONY DSC – S90 camera 25 frames/ s

Software:
HOBOware Lite® 3.3.3– reading out logger data
Adobe Premiere Pro CS5.5 ® - video analysis
MS Excel 2010 & STATISTICA 8.0 - data processing
Summary of gathered data
Total time
Acc.
No. of
Mean ± SD
(s)
readings
sequences
time (s)
/sequence
Lying
1,800
7,784
6
637.8 ± 511.8
Standing
268.9
8,872
6
44.8 ± 20.60
Walking
102.4
3,402
13
7.9 ± 5.9
Trotting
78.0
2,595
19
4.1 ± 1.39
Running
83.8
2,787
18
4.7 ± 3.78
Single Stride analysis - WALKING
4,000
3,000
2,000
Accel. g
X axis
1,000
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
103
106
109
112
115
118
121
124
127
130
133
0,000
No. Readings
-1,000
-2,000
 94 stride patterns on the vertical (x) axis
 8 key acceleration points (KAP)
Single Stride analysis – WALKING
Stride Acceleration Model
Single Stride analysis – WALKING
Kinematic stride parameters
Stance phase
Swing
phase
Stance
phase
ST(tw)  ST 4(ts )  SW 4(ts 1) ; ST(tw)  ST 4(ts )  ( SW 4(ts 1)  0.03) ,if SW 4(ts 1)  0.03  ST 1(ts 1)
SW(tw)  SW 4( ts )  ST 4( ts ) ; SW(tw)  ( SW 4( ts )  0.03)  ST 4( ts ) ,if SW 4(ts )  0.03  ST 1(ts )
Single Stride analysis – WALKING
Kinematic stride parameters
Walking
Total strides
(n)
Mean ± SD
Range
CI 95%
Stance
phase (s)
77
0.52 ± 0.17
0.24 – 1.20
0.48 – 0.56
Swing phase
(s)
77
0.29 ± 0.06
0.18 – 0.45
0.28 – 0.31
Stride
duration (s)
77
0.81 ± 0.19
0.42 – 1.53
0.77 – 0.86
Duty factor
(%)
77
63.30 ± 6.98
44.44 – 80.00 61.71 – 64.88
 % error of number of strides comparing with the video of 2%(7% - 33%)
Single Stride analysis - RUNNING
4,000
3,000
X axis
2,000
1,000
0,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
No. Readings
-1,000
-2,000
-3,000
-4,000
 180 stride patterns on the vertical (x) axis
 7 key acceleration points (KAP)
Single Stride analysis – RUNNING
Stride Acceleration Model
Single Stride analysis – RUNNING
Kinematic stride parameters
Stance
phase
Swing phase
Stance
phase
ST(tr )  ST 2( ts )  SW 4( ts 1) ; ST(tr )  ST 2( ts )  ( SW 4( ts 1)  0.03) ,if SW 4(ts 1)  0.03  ST 1A( ts )
SW(tr )  SW 4(ts )  ST 2( ts ) ; SW(tr )  ( SW 4( ts )  0.03)  ST 2 ,if SW 4(ts )  0.03  ST 1B(ts )
Single Stride analysis – RUNNING
Kinematic stride parameters
Running
Total strides
(n)
Mean ± SD
Range
CI 95%
Stance
phase (s)
161
0.13 ± 0.05
0.06 – 0.27
0.12 – 0.14
Swing phase
(s)
161
0.27 ± 0.08
0.12 – 0.48
0.26 – 0.29
Stride
duration (s)
161
0.40 ± 0.08
0.21 – 0.69
0.39 – 0.42
Duty factor
(%)
161
32.51 ± 10.42
12.50 – 66.67 30.89 – 34.13
 % error of number of strides comparing with the video of 1%(9% - 33%)
Gait discrimination
 Walking
 Trotting
 Running
Gait discrimination
Sequences for walking, trotting & running
77 Epochs – 3 sec. periods ≈ 100 acceleration readings
Acceleration values
Acceleration categories
-4
-3
-2
-1
0
1
2
3
4
5
Relative frequencies of acceleration categories (RFAC)
6
Gait discrimination
 Discriminant function of RFAC for the vertical, horizontal
axes & Sum vector
Acceleration Categories
Vertical (x)
axis
Horizontal
(y) axis
Sum Vector
0-1
1-2
2-3
-3 – (-2)
3-4
2-3
-4 – (-3)
-2 – (-1)
-2 – (-1)
0-1
2-3
5-6
1-2
3-4
Gait discrimination
 Classification function
S g  cg  wgAC1 * rf AC1  wgAC 2 * rf AC 2  .......wgACm * rf ACm
 Post hock classification matrix
• Vertical axis – correct cases 89.6%
• Horizontal axis – correct cases 90.9%
• Sum vector – correct cases 87.0%
• Trotting - least ; Walking – most accurate
 Optimized method for gait discrimination:
- using one axis and 4 or 5 AC identifies the gait type
Standing vs Lying
X Axis
Y Axis
Sum Vector
Standing
1,2
Lying
1,0
0,8
Acceleration (g)
0,6
0,4
0,2
0,0
-0,2
-0,4
-0,6
Sheep 1
Sheep 2
Ram 1
Ram 2
Sheep 3
Ram 3
SL 1
SL 2
SL 3
SL 4
SL 5
SL 6
SL 7
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