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  <doc>
    <id>1005</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>23</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>16</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Emerald</publisherName>
    <publisherPlace>Bingley</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-01-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups</title>
    <parentTitle language="eng">International Journal of Pervasive Computing and Communications</parentTitle>
    <identifier type="issn">1742-7371</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1108/IJPCC-03-2019-0017</enrichment>
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    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Phani Krishna</first_name>
      <last_name>Yalavarthi</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Philipp</first_name>
      <last_name>Wintersberger</last_name>
    </author>
    <author>
      <first_name>Clemens</first_name>
      <last_name>Schartmüller</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearable devices</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>physiological measures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver drowsiness detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>advanced driver assistance systems (ADAS)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simulator study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>active safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>heart rate variability (HRV)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>5946</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>24</pageNumber>
    <edition/>
    <issue>10</issue>
    <volume>15</volume>
    <articleNumber>5302</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fusion of Driving Behavior and Monitoring System in Scenarios of Driving Under the Influence: An Experimental Approach</title>
    <abstract language="eng">Driving under the influence of alcohol (DUI) remains a leading cause of accidents globally, with accident risk rising exponentially with blood alcohol concentration (BAC). This study aims to distinguish between sober and intoxicated drivers using driving behavior analysis and driver monitoring system (DMS), technologies that align with emerging EU regulations. In a driving simulator, twenty-three participants (average age: 32) completed five drives (one practice and two each while sober and intoxicated) on separate days across city, rural, and highway settings. Each 30-minute drive was analyzed using eye-tracking and driving behavior data. We applied significance testing and classification models to assess the data. Our study goes beyond the state of the art by a) combining data from various sensors and b) not only examining the effects of alcohol on driving behavior but also using these data to classify driver impairment. Fusing gaze and driving behavior data improved classification accuracy, with models achieving over 70% accuracy in city and rural conditions and a Long Short-Term Memory (LSTM) network reaching up to 80% on rural roads. Although the detection rate is, of course, still far too low for a productive system, the results nevertheless provide valuable insights for improving DUI detection technologies and enhancing road safety.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-59467</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/app15105302</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Jan-Philipp</first_name>
      <last_name>Göbel</last_name>
    </author>
    <author>
      <first_name>Niklas</first_name>
      <last_name>Peuckmann</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
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    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5946/applsci-15-05302-v2.pdf</file>
  </doc>
  <doc>
    <id>950</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>21</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>20</volume>
    <articleNumber>1029</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-02-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection</title>
    <abstract language="eng">Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (&gt;92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9506</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s20041029</enrichment>
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    <enrichment key="THI_openaccess">ja</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Nikoletta</first_name>
      <last_name>Sofra</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>drowsiness detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simulator study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>physiological measures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearable sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <collection role="persons" number="59914">Sofra, Nikoletta</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/950/sensors-20-01029-v2-1.pdf</file>
  </doc>
  <doc>
    <id>2589</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>229</pageFirst>
    <pageLast>236</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation</title>
    <parentTitle language="eng">Proceedings: 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications</parentTitle>
    <identifier type="isbn">978-1-4503-5946-7</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3239060.3239073</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">AutomotiveUI '18: 10th International Conference on Automotive User Interfaces and Interactive Vehicular Application, Toronto (Canada), 23.-25.09.2018</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Nikoletta</first_name>
      <last_name>Sofra</last_name>
    </author>
    <author>
      <first_name>Klemens</first_name>
      <last_name>Weigl</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automated Driving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver drowsiness detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simulator study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Subjective methods</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <collection role="persons" number="59914">Sofra, Nikoletta</collection>
  </doc>
  <doc>
    <id>2055</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>369</pageFirst>
    <pageLast>379</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-03-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Driver drowsiness in automated and manual driving: insights from a test track study</title>
    <parentTitle language="eng">IUI 2020: Proceedings of the 25th International Conference on Intelligent User Interfaces</parentTitle>
    <identifier type="isbn">978-1-4503-7118-6</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3377325.3377506</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">IUI '20: 25th International Conference on Intelligent User Interfaces, Cagliari (Italy), 17.-20.03.2020</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Nikoletta</first_name>
      <last_name>Sofra</last_name>
    </author>
    <author>
      <first_name>Klemens</first_name>
      <last_name>Weigl</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver drowsiness detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>field study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>subjective methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearables</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <collection role="persons" number="59914">Sofra, Nikoletta</collection>
  </doc>
  <doc>
    <id>6073</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>433</pageFirst>
    <pageLast>440</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <type>conferenceobject</type>
    <publisherName>Gesellschaft für Informatik</publisherName>
    <publisherPlace>Regensburg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-07-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Robust Drowsiness Detection Method based on Vehicle and Driver Vital Data</title>
    <parentTitle language="eng">Mensch und Computer 2017 - Workshopband</parentTitle>
    <enrichment key="THI_conferenceName">Mensch und Computer 2017: MuC'17 Workshops, Regensburg (Germany), 10.-13.09.2017</enrichment>
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    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.18420/muc2017-ws09-0307</enrichment>
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    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <editor>
      <first_name>Manuel</first_name>
      <last_name>Burghardt</last_name>
    </editor>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <editor>
      <first_name>Raphael</first_name>
      <last_name>Wimmer</last_name>
    </editor>
    <author>
      <first_name>Nikoletta</first_name>
      <last_name>Sofra</last_name>
    </author>
    <editor>
      <first_name>Christian</first_name>
      <last_name>Wolff</last_name>
    </editor>
    <editor>
      <first_name>Christa</first_name>
      <last_name>Womser-Hacker</last_name>
    </editor>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
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    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <collection role="persons" number="59914">Sofra, Nikoletta</collection>
  </doc>
  <doc>
    <id>2064</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>117</pageFirst>
    <pageLast>125</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-03-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: a Feasibility Analysis</title>
    <parentTitle language="eng">UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization</parentTitle>
    <identifier type="isbn">978-1-4503-6861-2</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3340631.3394852</enrichment>
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    <enrichment key="THI_conferenceName">UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization, Genua (Italy), 14.-17.07.2020</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>drowsiness detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>test track study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>physiological measures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearable devices</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>2069</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber>LBW2418</articleNumber>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
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