![](bg8.png)
high as compared to resting, and it also stayed high
in whole stress session, shown in figure 3 and 4.
5 CONCLUSIONS AND FUTURE
PERSPECTIVE
User’s psychophysiological state was measured and
predicted in real time and autonomy is provided to
the system to improve its interface dynamically with
respect to the mental workload and stress level on
the user. A PAUI was created, that performs
dynamic updations to its interface and helps in
decelerating the effects of workload and stress.
Moreover, the classification findings are quite
impressive. We have explored different aspects of
psychophysiology and combined them with external
emotional and attentional clues. Getting 88.37% of
accuracy in Person specific data and 84.75%
accuracy in Task specific data with this small
amount of training samples gives a valid indication
of having huge potential of improvement.
Current findings clearly suggest that the use of
Deep learning techniques could be a promising
measure to achieve higher degree of accuracy in
emotion classification.
Future aspects of this research are with the
improvements in emotion classification techniques
with current state of the art classifiers. One
important field to scrutinize is with Recurrent Neural
Networks that could be helpful in understanding the
changing patterns of the data and make prediction on
them. And to introduce more emotional states for
classification which helps in bring more dynamicity
and understandability to PAUI.
ACKNOWLEDGEMENTS
This work was supported from Fundação para a
Ciência e a Tecnologia (FCT, Portugal), through
project UID/EEA/50009/2013.
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Physiology, Facial Expressions and Eye Movements
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