Behaviour
|
Performance metric
|
Classifier algorithm
|
---|
1-min window
|
Decision-tree
|
K-means
|
HMM
|
SVM
|
Lying
|
Sensitivity
|
74.09
|
85.93
|
90.17
|
92.91
|
|
Precision
|
96.57
|
91.88
|
85.41
|
89.65
|
Standing
|
Sensitivity
|
82.08
|
59.50
|
38.35
|
51.65
|
|
Precision
|
47.01
|
29.28
|
37.28
|
77.01
|
Feeding
|
Sensitivity
|
95.65
|
59.92
|
83.83
|
98.01
|
|
Precision
|
92.03
|
86.13
|
91.54
|
91.01
|
Overall
|
Sensitivity
|
83.94
|
68.45
|
70.78
|
80.85
|
|
Precision
|
78.53
|
69.09
|
71.41
|
85.89
|
5-min window
| | | | |
Lying
|
Sensitivity
|
74.09
|
55.37
|
80.31
|
92.91
|
|
Precision
|
97.95
|
98.10
|
94.51
|
91.66
|
Standing
|
Sensitivity
|
88.46
|
69.23
|
76.92
|
60.89
|
|
Precision
|
47.92
|
69.23
|
54.05
|
79.15
|
Feeding
|
Sensitivity
|
97.44
|
99.36
|
97.44
|
98.29
|
|
Precision
|
93.25
|
87.08
|
93.25
|
92.36
|
Overall
|
Sensitivity
|
86.66
|
74.65
|
84.89
|
84.03
|
|
Precision
|
79.71
|
84.80
|
80.60
|
87.72
|
10-min window
| | | | |
Lying
|
Sensitivity
|
77.42
|
80.65
|
70.97
|
89.60
|
|
Precision
|
98.63
|
96.15
|
100.00
|
93.35
|
Standing
|
Sensitivity
|
88.00
|
76.00
|
92.00
|
68.00
|
|
Precision
|
55.00
|
59.38
|
50
|
76.04
|
Feeding
|
Sensitivity
|
98.78
|
98.78
|
100
|
100.00
|
|
Precision
|
93.10
|
90.00
|
93.18
|
93.18
|
Overall
|
Sensitivity
|
88.06
|
85.14
|
87.65
|
85.86
|
|
Precision
|
82.24
|
81.84
|
81.06
|
87.52
|
- Performance measures (sensitivity and precision) were obtained using 1-min, 5-min and 10-min windows. HMM refers to the hidden Markov model, and SVM refers to the support vector machine algorithm. Overall sensitivity is calculated as the arithmetic mean sensitivity for the three behaviours. Overall precision is calculated in a similar manner
- Values marked in bold indicate the best performing algorithm for each behaviour classification