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

  • 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.

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Metadaten
URN:urn:nbn:de:kobv:526-opus4-19803
Publisher DOI:https://doi.org/10.3390/app142411997
Author:Alina NechyporenkoORCiD, Marcus FrohmeORCiDGND, Yaroslav StrelchukORCiD, Vladyslav OmelchenkoORCiD, Vitaliy GarginORCiD, Liudmyla Ishchenko, Victoriia AlekseevaORCiD
Parent Title (English):Applied Sciences
Document Type:Article
Language:English
Year of Publication:2024
Volume:14
Issue:24
Article Number:11997
Publisher:MDPI
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Publishing Institution:Technische Hochschule Wildau
Tag:galvanic skin response; machine learning; photoplethysmography; stress
Source: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
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 616 Krankheiten
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2025/01/02
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