Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection.

Area of Science:

• Biomedical Engineering
• Virtual Reality Technology
• Psychophysiology

Background:

• Virtual reality (VR) offers immersive environments for phobia therapy.
• Accurate measurement of psychophysiological responses is crucial for assessing treatment efficacy.
• Existing protocols for biophysical signal acquisition in VR may lack standardization.

Purpose of the Study:

• To propose and validate a protocol for acquiring and processing biophysical signals in VR-based phobia therapy.
• To ensure reliable data for estimating user anxiety levels during VR exposure.
• To enhance the precision of anxiety assessment in virtual environments.

Main Methods:

• Developed a protocol for biophysical signal acquisition and processing within VR.
• Analyzed data from seven subjects exposed to heights in a VR environment.
• Utilized a nonlinear function with extracted biophysical signal features to estimate anxiety.
• Employed feature extraction in both time and frequency domains.

Main Results:

• The protocol facilitates effective measurement and processing of biophysical signals.
• Anxiety levels were estimated in real-time using a nonlinear function of signal features.
• Highest classification accuracy for anxiety levels was achieved using seven specific features.
• Optimal features included combinations of heart rate and electrodermal activity from time and frequency domains.

Conclusions:

• The proposed protocol enables robust biophysical signal analysis for VR applications.
• Accurate anxiety estimation in VR phobia therapy is feasible using integrated biophysical signals.
• Feature selection from heart rate and electrodermal activity significantly improves anxiety classification accuracy.