SYSTEM FOR MENTAL STRESS DETECTION AND CLASSIFICATION

Due to a rapidly changing lifestyle and increasing workload, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. Continuous stress monitoring will help users better understand their stress patterns and provide physicians with more reliable data for interventions. Stress and fatigue can be monitored by measuring physiological parameters like Electrocardiogram (ECG), and Galvanic Skin Response (GSR) continuously over a period. Autonomic Nervous System (ANS) primarily depends on the emotional responses of the human body to the dynamic surrounding. As a result of this fact, bio-signal recordings reflecting the operating condition of the physiological systems can provide useful information representing the dynamic mental stress levels. In this paper, I gathered baseline physiological measurements of Electrocardiogram (ECG), and Galvanic Skin Response (GSR) signals while users were subjected to multiple mental stressors. Raw physiological signals available at the PHYSIONET website were used to train the classifiers for stress interference. I classified the affective states as “Low Stress”, “Moderate Stress” and “High Stress” using features extracted from ECG and GSR. By using a combination of both ECG and GSR features I was able to obtain a prediction accuracy of more than 90 %