Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.

Dongrae Cho,Nak-Kyu Lee,Jooyoung Oh,Boreom Lee,Sayup Kim,Jinsil Ham,Jeanho Park

doi:10.3390/s17102435

Abstract

Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

Highlights

Virtual reality (VR) involves creating and implementing a simulated, realistic, three-dimensional environment [1]
The results showed that the optimal parameters (C, γ) were (102, 10−3 ), (103, 10), (104, 10), and (107, 10−1 ) in heart rate variability (HRV) + kernel-based extreme learning machine (K-extreme learning machine (ELM)), skin conductance (SC) + K-ELM, skin temperature (SKT) + K-ELM, and integrated features (IT) + K-ELM, respectively
Seven features such as HRavg, NNavg, pNN50, SCLavg, SCRmax and SKTavg were significantly different from each other (p-value < 0.05)

Summary

Introduction

Virtual reality (VR) involves creating and implementing a simulated, realistic, three-dimensional environment [1]. Diverse virtual environments can be constructed in limited spaces by generating realistic images, sounds, and other sensations. Since environments generated by VR devices are similar to the real world, they have been used in various fields, especially as treatment options in hospitals. VR devices have been used for social-adaptation training for social phobias, as well as for treating post-traumatic stress disorder (PTSD) [2]. Many researchers have utilized VR devices during their experiments to create environments and observe the corresponding responses [3,4,5]. Electroencephalogram (EEG) signals were measured in a VR environment, which are composed of three different traffic light situations (red, green, and yellow), and EEG signals

Results

Discussion

Conclusion