Discrimination of multiple stress levels in virtual reality environments using heart rate variability

People are suffering from various stress during daily living. Stress can cause a variety of symptoms, and in severe cases, it can lead to a dangerous disease. For this reason, it is essential to develop a simple method to evaluate stress level precisely. Popularly, heart rate variability (HRV) is used because it can reflect autonomic nervous system (ANS) activity. On the other hand, virtual reality (VR), which can provide environments similar to reality, is widely used in laboratory-based experiments. In this paper, we analyzed the HRV of healthy people by using the photoplethysmogram (PPG) while providing diverse stress situations. To detect and classify the exact stress levels, extracted HRV features and linear discriminant analysis (LDA) were utilized. As a result, high multi-class classification accuracy was obtained: Baseline (74%), mild stress (81%), and severe stress (82%).