Systems, Man & Cybernetics - April 2017 - 15
Table 2. A confusion matrix of prediction results
(per person per day).
Day 1
Day 2
Day 3
Day 4
Day 5
negative
0
Positive
33
negative
6
Positive
\
4
5
15
3
36
39
12
2
37
32
0
39
0
39
4
0
0
16
39
0
39
0
39
0
0
39
29
19
18
39
39
0
39
19
5
35
0
24
19
0
13
38
33
4
35
15
24
0
\
38
0
21
16
13
ES 2
39
22
ES 1
0
Positive
5
ES 2
negative
3
ES 1
39
ES 2
27
Positive
ES 1
negative
2
ES 2
34
1
ES 1
Positive
Actual
ES 2
Predicted
ES 1
Individual #
And this is logical, because different
humans react differently, and even the
same human brings different psychological baggage to the experiment from
one day to the next.
The results (in the form of a confusion matrix) of applying the autonomous data cloud formation method and
the AnYa type self-learning FRB zeroorder classifier are shown in Table 2.
The corresponding classification accuracies are in Table 3. The evolution of
the fuzzy rules based on the data from
individuals 7, 8, 9, and 10 measured on
the last day are presented in Table 4,
where we can see that the final fuzzy
rules changed in comparison to their
initial values based on the first and
only training data pair per class.
The overall classification rate we
achieved is approximately 75%, with
some people (individuals 2, 3, 4, 5, and
7) being predictable, while others
(individuals 1, 8, and 9) were more complex and therefore more difficult to predict, especially on some days. First,
all the predictions were conducted
based on only two features (heart rate
and skin conductance). But the human
body is very complex, and two features are far from sufficient to properly describe the subjects' reactions. As
a result, we need to further study the
human body and collect additional
related data. Second, in our experiments, the environment and other
factors that might have inf luenced
the results were neglected to simplify the modeling process. However,
those factors can be very important.
For example, if someone washed his
hands before the experiments, his
sk i n resi st a nce cou ld be la rgely
changed. Moreover, in many cases,
the wearable sensors we used in the
experiments failed to give very reliable readings. This problem could
be solved by using more precise sensory devices.
0
36
3
39
9
30
21
39
0
1
0
39
0
38
0
38
1
38
0
36
0
35
3
36
25
39
0
negative
\
\
0
35
0
32
0
38
0
39
Positive
33
6
\
\
6
25
39
0
39
0
negative
6
33
\
\
8
31
0
39
1
38
Positive
38
1
39
0
25
13
39
0
32
7
negative
33
6
0
38
22
17
0
39
29
10
Positive
27
12
7
31
32
6
27
12
39
0
negative
33
3
1
28
20
19
6
33
0
39
Positive
36
3
3
36
3
36
23
16
39
0
negative
0
39
36
3
24
15
31
8
0
39
Positive
39
0
25
13
32
6
39
0
34
5
negative
0
26
23
16
27
12
1
38
30
7
6
7
8
9
10
Table 3. Prediction accuracy (per person per day
and overall).
Prediction Accuracy
Individual
Number
Day 1
Day 2
Day 3
Day 4
Day 5
Overall
1
0.5897
0.7564
0.0933
1.0000
0.8462
0.6481
2
1.0000
1.0000
1.0000
0.2537
1.0000
0.8458
3
1.0000
1.0000
0.4935
1.0000
0.9870
0.8741
4
0.8571
0.9459
0.6316
1.0000
0.9600
0.8501
5
\
1.0000
0.7681
0.6711
1.0000
0.8226
0.5286
1.0000
0.9870
0.8269
1.0000
0.5455
1.0000
0.5385
0.7111
0.5224
0.6623
0.7692
1.0000
0.6430
0.9615
0.0769
0.2308
0.3924
1.0000
0.5200
1.0000
0.5325
0.5714
0.9872
0.5395
0.7166
6
0.8462
7
0.5641
8
0.4000
9
10
Conclusion
We studied and experimented in a laboratory environment with real individuals on the learning of human
perceptions of visual and auditory inputs-and we extrapolated to the other three types of sensory stimuli. We
argued that the perceptions and reactions are individual
\
as well as context and time dependent, but are predictable and stable during a given session. Moreover,
we proposed a systematic mathematical and computational cybernetic model as well as a fully autonomous
methodology to cluster and classify the emotional
Ap ri l 2017
IEEE SyStEmS, man, & CybErnEtICS magazInE
15
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