Stress Assessment in Working Professionals

Abstract

Overstress at work is a serious issue that affects both workers and companies. Stress impairs employees' well-being, reduces productivity, and raises absenteeism. Consequently, there may be an increase in organizational costs associated with subpar work output and sick leave. Additionally, high blood pressure brought on by stress and other factors may be harmful to heart health. A stress detector uses appropriate sensors, such as an electrocardiogram (ECG) or a galvanic skin response (GSR), to gather physiological signals to distinguish between a stressed and a normal person. The desired features that represent the stress level in working people are extracted from these signals through pre-processing. To categorize this extracted feature set, support vector machine (SVM) and k-nearest neighbour (KNN) are examined. According to the outcome, the feature vector with the best features has a significant impact on stress detection.