Physiological Signal Analysis and Stress Classification from VR Simulations Using Decision Tree Methods

• Published in: Bioengineering (Basel) Vol. 10; no. 7; p. 766
• Main Authors: Ishaque, Syem, Khan, Naimul, Krishnan, Sridhar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.06.2023; MDPI
• Subjects: Accuracy; Algorithms; Bioengineering; Cardiac muscle; Classification; Computer & video games; Computer applications; Customization; Data acquisition; Decision analysis; Decision tree; Decision trees; EKG; Electrocardiography; Entropy; Exocrine glands; Galvanic skin response; Gini index; Heart rate; HRV; Immune system; K-means feature; Low frequencies; Nervous system; Parasympathetic nervous system; personalized CART model; Physiology; Regression analysis; Regression models; Respiration; Roller coasters; Signal analysis; Signal classification; Skin; Social skills; stress; Stress analysis; Virtual reality; VR video game; United States; stress; K-means feature; Gini index; personalized CART model; HRV; VR video game
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10070766

Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against these symptoms, it is important to assess the impact of stress due to various activities, which is initially determined through the change in the sympathetic (SNS) and parasympathetic (PNS) nervous systems. After acquiring physiological data wirelessly through captive electrocardiogram (ECG), galvanic skin response (GSR) and respiration (RESP) sensors, 21 time, frequency, nonlinear, GSR and respiration features were manually extracted from 15 subjects ensuing a baseline phase, virtual reality (VR) roller coaster simulation, color Stroop task and VR Bubble Bloom game. This paper presents a comprehensive physiological analysis of stress from an experiment involving a VR video game Bubble Bloom to manage stress levels. A personalized classification and regression tree (CART) model was developed using a novel Gini index algorithm in order to effectively classify binary classes of stress. A novel K-means feature was derived from 11 other features and used as an input in the Decision Tree (DT) algorithm, strong learners Ensemble Gradient Boosting (EGB) and Extreme Gradient Boosting (XGBoost (XGB)) embedded in a pipeline to classify 5 classes of stress. Results obtained indicate that heart rate (HR), approximate entropy (ApEN), low frequency and high frequency ratio (LF/HF), low frequency (LF), standard deviation (SD1), GSR and RESP all reduced and high frequency (HF) increased following the VR Bubble Bloom game phase. The personalized CART model was able to classify binary stress with 87.75% accuracy. It proved to be more effective than other related studies. EGB was able to classify binary stress with 100% accuracy, which outperformed every other related study. XGBoost and DT were able to classify five classes of stress with 72.22% using the novel K-means feature. This feature produced less error and better model performance in comparison to using all the features. Results substantiate that our proposed methods were more effective for stress classification than most related studies.