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, hi...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/11997 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850059545579094016 |
|---|---|
| author | Alina Nechyporenko Marcus Frohme Yaroslav Strelchuk Vladyslav Omelchenko Vitaliy Gargin Liudmyla Ishchenko Victoriia Alekseeva |
| author_facet | Alina Nechyporenko Marcus Frohme Yaroslav Strelchuk Vladyslav Omelchenko Vitaliy Gargin Liudmyla Ishchenko Victoriia Alekseeva |
| author_sort | Alina Nechyporenko |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bcff341aa04b4f9f8c36e0ebed25933f |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-bcff341aa04b4f9f8c36e0ebed25933f2025-08-20T02:50:52ZengMDPI AGApplied Sciences2076-34172024-12-0114241199710.3390/app142411997Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable SensorsAlina Nechyporenko0Marcus Frohme1Yaroslav Strelchuk2Vladyslav Omelchenko3Vitaliy Gargin4Liudmyla Ishchenko5Victoriia Alekseeva6Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, GermanyDivision Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, GermanyDivision Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, GermanyDivision Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, GermanyDepartment of Otorhinolaryngology, Department of Pathological Anatomy, Kharkiv National Medical University, 4 Nauky Avenue, 61000 Kharkiv, UkraineDivision Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, GermanyDivision Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, 1 Hochschulring, 15745 Wildau, GermanyThis 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.https://www.mdpi.com/2076-3417/14/24/11997galvanic skin responsephotoplethysmographystressmachine learning |
| spellingShingle | Alina Nechyporenko Marcus Frohme Yaroslav Strelchuk Vladyslav Omelchenko Vitaliy Gargin Liudmyla Ishchenko Victoriia Alekseeva Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors Applied Sciences galvanic skin response photoplethysmography stress machine learning |
| title | Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors |
| title_full | Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors |
| title_fullStr | Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors |
| title_full_unstemmed | Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors |
| title_short | Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors |
| title_sort | galvanic skin response and photoplethysmography for stress recognition using machine learning and wearable sensors |
| topic | galvanic skin response photoplethysmography stress machine learning |
| url | https://www.mdpi.com/2076-3417/14/24/11997 |
| work_keys_str_mv | AT alinanechyporenko galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors AT marcusfrohme galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors AT yaroslavstrelchuk galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors AT vladyslavomelchenko galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors AT vitaliygargin galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors AT liudmylaishchenko galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors AT victoriiaalekseeva galvanicskinresponseandphotoplethysmographyforstressrecognitionusingmachinelearningandwearablesensors |