Analysis of Context Histories for Classification of Mental Stress in Real Situations by means of the Heart Rate Variability

• Rodrigo Bavaresco UNISINOS
• Jorge Barbosa UNISINOS

DOI: https://doi.org/10.5753/sbcup.2019.6586

Abstract

Mental stress is a major cause of physical and emotional health pro- blems in the world. In this way, it becomes strategic to measure daily stress throught physiology, using these measures in applications that aim individuals well-being. However, daily motion influences physiological data collection, producing noise. This work collects and analyzes the contexts histories of 5 individuals with the purpose of classifying mental stress through heart rate variability. As a scientific contribution, the method identifies and removes noise, as well as shows a relation of the stress levels and the location in which they were measured. The best performance, among three machine learning algorithms was obtained from Support Vector Machine classifier, resulting in 82% of accuracy.

Keywords: Classification, Mental Stress, Heart Rate

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