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

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 commercial, off-the-shelf 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 85% to 92%. This approach of measuring stress is very suitable for real-time applications and can be used by anybody who wants to improve their performance.