Psychological Stress Detection Based on Physiological Signals
Date Issued
2016
Date
2016
Author(s)
Yeh, Chau-Che
Abstract
Psychological stress is one of the major causes of physical and psychological diseases in the modern world that more than 70% hospital visitors are linked to it. Stress also results more than 200 billion economic loss in U.S. Therefore, we hope the stress research can detect stress in daily life in the future and improve health and well-beings of humankind. We did 2 experiments in this research. Experiment 1 figures out the affective stimuli from relaxed to stressful is Quadrant 4 Quadrant 1 Quadrant 3 Quadrant 2. Experiment 2 proves that the stimuli sequence works. We proposed a method to choose appropriate affective multimedia contents to be stressors of the stress stimuli system. And we also figure out some good features and classification process for stress classification. Feature selection does help in reduce feature numbers of each physiological signals and improve the classification accuracies. Self-assessment is subjective label that can’t provide precise stress labels for classification. After we merge into 2 classes of stress, the maximum accuracy of classification is approximately 78%, improved about 34% in general. In this research, we proposed a completed process to research psychological stress, includes stress stimuli selection, stress stimulation, physiological signal collection, physiological signal analysis, and psychological stress recognition and prediction via physiological signals. Because of respiration is the only physiological signal which we can control by our will to reduce stress and adjust emotion. We recommend GSR and BVP is the best physiological signal to measure stress in wearable devices.
Subjects
Psychological stress
Physiological signal
EEG
GSR
Heart rate
Respiration
Wearable
SDGs
Type
thesis