Abstract
Stress is an inevitable part of our lives in modern society since in many situations people are exposed to various stressors daily. According to studies, long-term stress can cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, stress detection is one of the crucial areas of study to maintain a healthy life. Recently, by developing commercial wearable technologies, real-time and continuous data collection for personal stress monitoring becomes more feasible. Under stress conditions, there are notable changes in physiological signals such as heart rate, respiration, perspiration, and eye pupil dilation. Previous studies have shown that Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), can identify stress. EDA measures changes in perspiration by detecting the changes in the electrical conductivity of the skin. This paper focuses on stress detection using only EDA wearable sensors and applied machine learning techniques. First, 87 different features are extracted from EDA signals. Then, the data are normalized per subject because of differences in individuals’ physiological responses. Finally, five dominant features in stress detection are selected. We used a publicly available dataset, namely, the wearable stress and affect detection dataset (WESAD) in this study. The results show that the One-Leave-Out method is capable of detecting stress with 97.03% accuracy.