Psychophysiological signals associated with affective states
Maurizio Mauri1, Valentina Magagnin, Pietro Cipresso
1IULM University of Milan, Via Carlo Bo 2, 20143, Milan, Italy. maurizio.mauri@iulm.it
Summary
This study developed a protocol for classifying affective states during human-computer interaction using physiological signals. Preliminary findings show specific patterns in respiration, skin response, and brain activity correlate with relaxation, engagement, and stress.
Area of Science:
- Human-Computer Interaction
- Affective Computing
- Psychophysiology
Background:
- Automatic classification of affective states is crucial for adaptive human-computer interaction (HCI).
- Standardized protocols are needed to reliably measure and interpret psychophysiological responses in HCI contexts.
Purpose of the Study:
- To develop an optimal standard protocol for automatic classification of affective states in HCI.
- To compare psychological test reports with quantitative psychophysiological measures.
- To identify specific psychophysiological patterns associated with relaxation, engagement, and stress.
Main Methods:
- Quantitative analysis of psychophysiological signals (respiration, galvanic skin response, blood volume pulse, electrocardiogram, electroencephalogram) from 43 healthy students.
- Non-invasive wearable sensors were used for data acquisition during computer-mediated stimuli exposure.
- Stimuli were designed to elicit distinct affective states: relaxation, engagement, and stress.
Main Results:
- The quantitative analysis refined the protocol for affective state classification.
- Preliminary findings identified specific psychophysiological patterns correlating with relaxation, engagement, and stress.
- Certain quantitative measures show potential for characterizing distinct affective states.
Conclusions:
- The developed protocol shows promise for objective affective state classification in HCI.
- Psychophysiological measures offer valuable insights into user affective experiences.
- Further research can refine these measures for enhanced HCI applications.
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