Proposed new signal for real-time stress monitoring: Combination of physiological measures

Human stress is a physiological tension that appears when a person responds to mental, emotional, or physical challenges. Detecting human stress and developing methods to manage it, has become an important issue nowadays. Automatic stress detection through physiological signals may be a useful method for solving this problem. In most of the earlier studies, longterm time window was considered for stress detection. Continues and realtime representation of the stress level is usually done through one physiological signal. In this paper, a realtime stress monitoring system is proposed which shows the user a new signal for feedback stress level. This signal is combined of weighted features of galvanic skin response and photoplethysmography signals. The features are defined in 20sec time windows. Correlation feature selection and linear regression methods are used for feature selection and feature combination respectively. Furthermore, a set of experiments was conducted for training and testing of the proposed model. The proposed model can represent the relative stress level perfectly and has 79% accuracy for classifying the stress and relaxation phases into two categories by a determined threshold.