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  <doc>
    <id>1980</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>24</issue>
    <volume>14</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19803</identifier>
    <enrichment key="opus.import.date">2025-01-02T07:01:24+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3390/app142411997</enrichment>
    <enrichment key="SourceTitle">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</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Yaroslav Strelchuk</author>
    <author>Vladyslav Omelchenko</author>
    <author>Vitaliy Gargin</author>
    <author>Liudmyla Ishchenko</author>
    <author>Victoriia Alekseeva</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>galvanic skin response</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>photoplethysmography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>stress</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1980/applsci-14-11997.pdf</file>
  </doc>
  <doc>
    <id>1989</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>196</pageFirst>
    <pageLast>202</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3892</volume>
    <type>conferenceobject</type>
    <publisherName>RWTH Aachen</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Detection of stress using photoplethysmography</title>
    <abstract language="eng">Stress is an important factor affecting human health, and its timely detection can significantly improve quality of life. This study addresses the current issue of identifying stress states using photoplethysmography signals obtained from the wearable Shimmer 3 sensor. The purpose of the study was to establish an effective approach for stress detection, specifically through the analysis of heart rate variability changes during different states: rest and stress. A distinctive feature of the research is the use of the air raid siren sound as a stress factor. The research methodology includes the collection and analysis of biosignals, allowing for the assessment of cardiovascular system functioning under the impact of stress factors. The results of the study demonstrated significant differences in heart rhythm indicators depending on the person's state, highlighting the potential of these metrics as a tool for health monitoring and stress detection. The paper proposes a new approach to using photoplethysmography for assessing stress responses, which may contribute to the development of personalized stress management methods.</abstract>
    <parentTitle language="eng">Proceedings of the 7th International Conference on Informatics &amp; Data-Driven Medicine (IDDM 2024)</parentTitle>
    <identifier type="url">https://ceur-ws.org/Vol-3892/short3.pdf</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-19896</identifier>
    <enrichment key="opus.import.date">2025-01-20T09:28:49+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="SourceTitle">Strelchuk, Y., Nechyporenko, A., Frohme, M., Gargin, V., Lupyr, A., &amp; Alekseeva, V. (2024). Detection of stress using photoplethysmography. In N. Shakhovska, J. Jiao, I. Izonin, &amp; S. Chretien, Proceedings of the 7th International Conference on Informatics &amp; Data-Driven Medicine (IDDM 2024) (pp. 196–202). Aachen: CEUR-WS. Retrieved from https://ceur-ws.org/Vol-3892/short3.pdf</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Yaroslav Strelchuk</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Vitaliy Gargin</author>
    <author>Andrii Lupyr</author>
    <author>Victoriia Alekseeva</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>air raid signal</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cardiovascular system health</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>early stress detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>health monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>heart rate variability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>personalized stress management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>photoplethysmography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>stress</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearable sensors</value>
    </subject>
    <collection role="ddc" number="613">Persönliche Gesundheit und Sicherheit</collection>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1989/short3.pdf</file>
  </doc>
</export-example>
