Stress detection in computer users through non-invasive monitoring of physiological signals.

he emerging discipline of Affective Computing pursues the development of computers that could interact with their users taking their affective states into account. For example, if a computer could detect when its user is experiencing stress, it could change the colors and sounds of its user interface to try to calm him/her down. Similarly, the pace of instruction in a computer-based training system could be adapted according to the stress level sensed in the pupil. The research described in this paper aims at the development of a stress detection approach based on automatic monitoring of physiological signals in the computer user. The paper describes the three main aspects of our work: experiment setup for physiological sensing, signal processing to detect the affective state and affective recognition using a learning system. Four signals: Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), Pupil Diameter (PD) and Skin Temperature (ST) are monitored and analyzed to differentiate affective states in the user, in a non-invasive fashion. Results indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in emotional state of our experimental subjects when stress stimuli are applied to the interaction environment.