Neuro-fuzzy physiological computing to assess stress levels in virtual reality therapy

G. Tartarisco, N. Carbonaro, A. Tonacci, G. M. Bernava, A. Arnao, G. Crifaci, P. Cipresso, G. Riva, A. Gaggioli, D. De Rossi, A. Tognetti, G. Pioggia

Research output: Contribution to journalArticlepeer-review


This paper reports the design and assessment of a neuro-fuzzy model to support clinicians during virtual reality therapy. The implemented model is able to automatically recognize the perceived stress levels of the patients by analyzing physiological and behavioral data during treatment. The model, consisting of a self-organizingmap and a fuzzy-rule-basedmodule, was trained unobtrusively recording electrocardiogram, breath rate and activity during stress inoculation provided by the exposure to virtual environments. Twenty nurses were exposed to sessions simulating typical stressful situations experienced at their workplace. Four levels of stress severity were evaluated for each subject by gold standard clinical scales administered by trained personnel. The model's performances were discussed and compared with the main machine learning algorithms. The neurofuzzy model shows better performances in terms of stress level classification with 83% of mean recognition rate.

Original languageEnglish
Pages (from-to)521-533
Number of pages13
JournalInteracting with Computers
Issue number5
Publication statusPublished - 2015


  • Artificial intelligence
  • Empirical studies in HCI
  • Human computer interaction
  • Physiological computingt
  • Stress recognition
  • Virtual reality

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software


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