Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices...

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Bibliographic Details
Published inElectronics (Basel) Vol. 10; no. 3; p. 301
Main Authors Durán-Acevedo, Cristhian Manuel, Carrillo-Gómez, Jeniffer Katerine, Albarracín-Rojas, Camilo Andrés
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2021
Subjects
Algorithms
Classification
Coronaviruses
COVID-19
Discriminant analysis
Electrocardiography
Electromyography
Electronic devices
Electronic noses
Engineering education
Galvanic skin response
Heart rate
Hormones
Measuring instruments
Medical research
Mental health
Muscles
Nervous system
Physiology
Quantitative psychology
Questionnaires
Skin
Stress
Stress relaxation
Students
Support vector machines
VOCs
Volatile organic compounds
United States > US
Texas
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Summary:This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10030301