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Applied Sciences
Volume 14
Issue 24
10.3390/app142411997
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Article

Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors

by
Alina Nechyporenko
1,2,
Marcus Frohme
1,
Yaroslav Strelchuk
1,
Vladyslav Omelchenko
1,2,
Vitaliy Gargin
3,4,
Liudmyla Ishchenko
1 and
Victoriia Alekseeva
1,3,4,*
1
Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, Germany
2
Systems Engineering Department, Kharkiv National University of Radio Electronics, 14 Nauky Avenue, 61166 Kharkiv, Ukraine
3
Department of Otorhinolaryngology, Department of Pathological Anatomy, Kharkiv National Medical University, 4 Nauky Avenue, 61000 Kharkiv, Ukraine
4
Department of Professionally Oriented Disciplines, Kharkiv International Medical University, 38 Molochna Str., 61001 Kharkiv, Ukraine
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(24), 11997; https://doi.org/10.3390/app142411997
Submission received: 2 November 2024 / Revised: 16 December 2024 / Accepted: 19 December 2024 / Published: 21 December 2024
(This article belongs to the Section Biomedical Engineering)
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Abstract

This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.
Keywords: galvanic skin response; photoplethysmography; stress; machine learning galvanic skin response; photoplethysmography; stress; machine learning

Share and Cite

MDPI and ACS Style

Nechyporenko, A.; Frohme, M.; Strelchuk, Y.; Omelchenko, V.; Gargin, V.; Ishchenko, L.; Alekseeva, V. Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors. Appl. Sci. 2024, 14, 11997. https://doi.org/10.3390/app142411997

AMA Style

Nechyporenko A, Frohme M, Strelchuk Y, Omelchenko V, Gargin V, Ishchenko L, Alekseeva V. Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors. Applied Sciences. 2024; 14(24):11997. https://doi.org/10.3390/app142411997

Chicago/Turabian Style

Nechyporenko, Alina, Marcus Frohme, Yaroslav Strelchuk, Vladyslav Omelchenko, Vitaliy Gargin, Liudmyla Ishchenko, and Victoriia Alekseeva. 2024. "Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors" Applied Sciences 14, no. 24: 11997. https://doi.org/10.3390/app142411997

APA Style

Nechyporenko, A., Frohme, M., Strelchuk, Y., Omelchenko, V., Gargin, V., Ishchenko, L., & Alekseeva, V. (2024). Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors. Applied Sciences, 14(24), 11997. https://doi.org/10.3390/app142411997

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