Correlation of Stress Inducers and Physiological Signals with Continuous Stress Annotations
This study proposes a methodology in building a multimodal stress-level model using different non-invasive physiological signals: Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), and Respiratory Variability (Resp). Paced Stroop, Mental Math, and Game were used to induce stress to 4 subjects. A fixed window size of 5 seconds and 1 second sliding size were used to segment the self-annotated data. Six statistical features were extracted from each physiological signals, giving a total of 18 features. Different classification and regression algorithms were used to get the correlation of the features and the stress labels. The results show very high correlation for all three stress inducers with Paced Stroop Test as the highest which in term means that all 3 activities are effective in inducing stress. Results also show a good correlation for the 3 physiological signals with Respiration as the highest among the 2 others, which indicate that Respiration provides better correlation even by just using low-level features.
KeywordsHeart Rate Variability Physiological Signal Test Subject Stress Inducer Pupil Diameter
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