Heart-rate Variability Analysis for Stress Assessment in a Video-Game Setup
thesisposted on 15.10.2021, 14:45 by Syem Ishaque
Chronic stress makes a person vulnerable to diseases leading to the evolution of various physical and mental health conditions including chronic fatigue, diabetes, obesity, depression and other symptoms associated with immune disorders. This study investigates the impact of virtual reality video games to reduce stress and increase resistance to stress. The study consisted of 4 phases: (1) a baseline phase which was used to assess the subjects normal physiological function, (2) a virtual reality roller coaster phase which was meant to induce stress through the elicitation of affective emotions (3) a cognitive color stroop test was also used to induce stress and increase anxiety, and (4) a VR fish game was required to understand the impact of playing video games to reduce stress and anxiety. The physiological variation associated with stress was analyzed through physiological signals such as Electrocardiogram (ECG), respiratory signal (RESP), and Galvanic Skin Response (GSR). Specific features such as pNN50, RMSSD, ApEn, LF, HF, LF/HF ratio and respiration rate were extracted from the corresponding signals. ECG derived features were used as input features for various machine learning models. Poincare plots were used to indicate abnormal HRV and stress through a visual representation. SD1 and SD2 share a high correlation with SNS and PNS activity, ApEn shares a 0.811 correlation with LF/HF ratio, proving that it is an effective method to assess stress. Average results from the users indicate that LF/HF reduced from 1.2 to 0.93, ApEn reduced from 0.0.695 to 0.562 after playing the VR fish game, demonstrating the positive influence of the game towards stress reduction. Statistical analysis using the t-test verified that the data were statistically significant, p-value < 0.05 rejected that the null hypothesis that the data was statistically significant. The Ensemble Gradient Boosting model was able to classify binary classes associated with stress/relaxation with 100% accuracy, various other models were able to classify with 90% accuracy. RUC curve, precision/recall curve and TP, FP, TN, FN revealed that few models such as Naive Bayes were inadequate for stress classification. The research study effectively demonstrated the impact of VR fish game for stress reduction.