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

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.

Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

“…Thus, associated physiological signals such as Heart Rate Variability (HRV), Galvanic Skin Response (GSR), Skin Temperature (ST), Electroencephalogram (EEG), Electrocardiogram (ECG), Blood Volume Pulse (BVP), a.o. reveals ANS activity (Cho et al, 2017;Seoane et al, 2014). The aforementioned physiological signals are considered to be reliable stress indicators (Karthikeyan et al, 2013) as they can contain information related to the intensity and the quality of the experience of a subject (Can et al, 2019).…”

“…Several studies attempt to detect stress states and the associated stress levels (low, medium and high). Most of these studies utilise GSR and ECG combined with ST, EMG or EEG (Healey & Picard, 2005;de Santos Sierra et al, 2011;Cho et al, 2017;Zhang, 2018;Liao et al, 2005). The classification of an affective state is performed using SVM, random forest, Bayesian network and fuzzy logic.…”

Biological-Signal-Based User-Interface System for Virtual-Reality Applications for Healthcare

“…In the medical field, they are used in monitoring systems for early detection of dangerous situations and diseases by monitoring the patient's health status and in medical automation systems that provide continuous treatment or rehabilitation services. Methods that use biological signals for automatic measurement of stress and objective data collection have achieved practical results [11,12]. The biological-signal interfaces being used in traditional medical systems are now available for the general public following the development of wearable biological-signal devices.…”

Voice control in mixed reality

“…However, it is gradually solved, as shown by the authors of [6], where the virtual keyboard was presented. Health problems such as stress or focusing too close to the display are constantly analyzed so that in the future everyone can safely and fully immerse into virtual world [7]. Nowadays, important issue is Internet of Things and its impact on our life [8], [9].…”