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Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game

Syem Ishaque · Alice Rueda · Binh Nguyen · Naimul Khan · Sridhar Krishnan

54 citations0/0 files storedAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Emotion and Mood Recognition Heart Rate Variability and Autonomic Control Non-Invasive Vital Sign Monitoring

Abstract

Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are moré discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.

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References

2018Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection 2019Classification of Perceived Mental Stress Using A Commercially Available EEG Headband 2017Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach 2017Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine 2010Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State

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