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A decision support system for real-time stress detection during virtual reality exposure.

Author information

1
ATN-P Lab., Istituto Auxologico Italiano, Milan, Italy.
2
National Research Council of Italy (CNR), Institute of Clinical Physiology (IFC), Italy.
3
"Antonio Zampolli" Institute for Computational Linguistics (ILC), Italy.
4
Research Center "E.Piaggio", University of Pisa, Via Diotisalvi 2, Pisa, Italy.
5
Information Engineering Department, University of Pisa, Via Caruso 2, Pisa, Italy.
6
Informatics and Telematics Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thermi, Thessaloniki, Greece.
7
Starlab Barcelona SL, Barcelona, Spain.

Abstract

Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.

PMID:
24732491
[Indexed for MEDLINE]

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