A Decision Support System for Real-Time Stress Detection During Virtual Reality Exposure

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.

[1]  Andrea Gaggioli,et al.  Toward a validation of cyber-interventions for stress disorders based on stress inoculation training: a systematic review , 2013, Virtual Reality.

[2]  Andrea Gaggioli,et al.  NeuroVR 2 - A Free Virtual Reality Platform for the Assessment and Treatment in Behavioral Health Care , 2011, MMVR.

[3]  J. J. McDonald,et al.  Stress, mental health, and cognition: a brief review of relationships and countermeasures. , 2007, Aviation, space, and environmental medicine.

[4]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Pietro Cipresso,et al.  Electro-Physiological Data Fusion for Stress Detection , 2012, Annual Review of Cybertherapy and Telemedicine.

[6]  Andrea Gaggioli,et al.  2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops a Mobile Biosensor to Detect Cardiorespiratory Activity for Stress Tracking , 2022 .

[7]  D. Tzovaras,et al.  Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection , 2012, PloS one.

[8]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[9]  D. Meichenbaum,et al.  Stress inoculation: a preventative approach. , 1985, Issues in mental health nursing.

[10]  Mario Tesconi,et al.  Wearable biomonitoring system for stress management: A preliminary study on robust ECG signal processing , 2011, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[11]  P. Silva,et al.  ENOBIO dry electrophysiology electrode; first human trial plus wireless electrode system , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Nicole Y. Weekes,et al.  The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health , 2007, Biological Psychology.

[13]  Andrea Gaggioli,et al.  Interreality in Practice: Bridging Virtual and Real Worlds in the Treatment of Posttraumatic Stress Disorders , 2009, Cyberpsychology Behav. Soc. Netw..

[14]  Branimir Dropuljic,et al.  Stress Inoculation Training Supported by Physiology-Driven Adaptive Virtual Reality Stimulation , 2009, Annual Review of Cybertherapy and Telemedicine.

[15]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .