Determination of Stress in Humans Using Data Fusion of Off-The-Shelf Wearable Sensors Data for Electrocardiogram and Galvanic Skin Response

Abstract

Stress detection helps individuals understand their stress levels and advises them when to take a break from activities causing stress. Physical activities and environmental influences can affect a person’s stress levels. People with professions as first responders, pilots, and working parents with newborns are examples of people exposed to a large amount of stress. Acquisition and proper analysis of physiological data is helpful in managing stress. In this paper, the results from two sensors, electrocardiogram (ECG) and galvanic skin response (GSR) measurements, are fused to analyze stress in individuals; these sensors are noninvasive and wearable. Data from these sensors are collected simultaneously over a period of 25 minutes from 25 people which are undergoing a simulated stressor. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used as the classifiers while Linear Discriminant Analysis (LDA) is used as the stress detection algorithm. The stress detection accuracy achieved varies with individuals and ranges from 87% to 95%. This approach of measuring stress is very suitable for real-time applications and can be used by practically anybody who wants to improve their performance.