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Figure 1
Example measurement of low frequency skin conductance. Person being highly stressed (red curve), moderately stressed (green curve), and totally relaxed (blue curve).
Figure 2
Conductance measurement. Example of data reported as disgust.
Figure 3
CWT treated conductance measurement. The same example (Figure 2) of data reported as disgust with CWT applied.
Figure 4
Architecture of the machine learning process.
Figure 5
Confusion matrix for the first results produced by the model using the full dataset.
Figure 6
Confusion matrix for the first results produced by the model using the full data set with SMOTE data added.
Figure 7
Confusion matrix for the results produced by the model using the three category data set with SMOTE data added.
Results using the full CWT applied EDA dataset with synthetic data. The parameters are set to: test size = 20 and trees = 100.
test size = 20
trees = 100
precision
recall
f1-score
support
amusement
1.00
0.50
0.67
4
anger
0.60
0.75
0.67
4
disgust
0.33
0.25
0.29
4
fear
1.00
1.00
1.00
6
neutral
0.80
0.44
0.57
9
sadness
0.27
0.60
0.37
5
accuracy
0.59
32
macro avg
0.67
0.59
0.59
32
weighted avg
0.70
0.59
0.61
32
Results using the 3 class CWT EDA data with synthetic data. Test size = 20 and trees = 300.
test size = 20
trees = 600
precision
recall
f1-score
support
amusement
1.00
0.80
0.89
5
disgust
0.80
0.67
0.73
6
sadness
0.71
1.00
0.83
5
accuracy
0.81
16
macro avg
0.84
0.82
0.82
16
weighted avg
0.84
0.81
0.81
16
Results using the CWT treated EDA data with test size = 20 and trees = 100.
test size = 20
trees = 100
precision
recall
f1-score
support
amusement
0.75
0.75
0.75
4
anger
0.00
0.00
0.00
2
disgust
1.00
0.25
0.40
8
fear
0.00
0.00
0.00
1
neutral
0.00
0.00
0.00
2
sadness
0.15
0.67
0.25
3
accuracy
0.35
20
macro avg
0.32
0.28
0.23
20
weighted avg
0.57
0.35
0.35
20
Category of emotion and how many times the emotion was reported.