Stress detection in computer users based on digital signal processing of noninvasive physiological variables

Area of Science:

• Physiological computing
• Affective computing
• Human-computer interaction

Background:

• Developing reliable emotion recognition systems is crucial for adaptive human-computer interaction.
• Non-invasive monitoring of physiological signals offers a promising avenue for objective stress detection.
• Previous research has explored various physiological markers for emotional state assessment.

Purpose of the Study:

• To develop and evaluate a stress detection system using non-invasive physiological sensors.
• To identify which physiological signals are most indicative of stress.
• To differentiate between 'stress' and 'relaxed' affective states in computer users.

Main Methods:

• Experiment setup for physiological sensing using non-intrusive sensors.
• Signal preprocessing for extracting affective features from galvanic skin response (GSR), blood volume pulse (BVP), pupil diameter (PD), and skin temperature (ST).
• Supervised classification using a support vector machine (SVM) to distinguish between stress and relaxed states.

Main Results:

• Physiological signals demonstrated a strong correlation with induced emotional changes during stress stimuli.
• Pupil diameter (PD) was identified as the most significant indicator of affective state.
• The developed system successfully differentiated between stress and relaxed states.

Conclusions:

• Non-invasive physiological monitoring can effectively detect stress in computer users.
• Pupil diameter is a key physiological marker for stress detection in human-computer interaction.
• This research contributes to the advancement of emotion recognition technologies for adaptive systems.