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

Andrea Gaggioli, Pietro Cipresso, Giuseppe Riva, Silvia Serino, Giovanni Pioggia, Gennaro Tartarisco, Giovanni Baldus, Daniele Corda, Marcello Ferro, Nicola Carbonaro, Alessandro Tognetti, Danilo De Rossi, Dimitris Giakoumis, Dimitrios Tzovaras, Alejandro Riera

Research output: Contribution to journalArticle

13 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)114-120
Number of pages7
JournalNot available
Publication statusPublished - 2014


  • stress
  • stress detection


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