<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>3075</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>242 - 252</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2024-10-31</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AI-based User Emotion Recognition from Interaction Data : Challenges and Guidelines for Training Data Creation</title>
    <abstract language="eng">Artificial Intelligence (AI) is a rising topic in the field of emotion recognition, e.g., from facial expressions. However, existing methods often require to be performed in staged set ups and are obtrusive by gathering additional data. Especially, collecting video data includes a high data protection risk. Our approach is to provide an unobtrusive emotion recognition tool based on Keystroke, Mouse and Touchscreen (KMT) data. Recently, we published a data set for emotion recognition from keystroke and mouse interaction data. In this paper, we present the challenges we faced during the creation of the data set. This covers collecting User Interface (UI) data as well as emotional ground truth data. For each of seven mentioned challenges, we provide our solutions as well as guidelines for other researchers to prevent them. The challenges include possible issues with recorded data as well as issues of automated facial coding engines. We provide a possible approach for manual facial coding and describe aspects attention should be paid to. Furthermore, we indicate issues when using different software tools to collect the data. The paper aims to help other researchers by providing insights and a guideline for the creation of the data set. We make these insights available for other researchers who want to create similar data sets or who want to expand ours. By sharing our insights, we aim to improve the reproducibility of AI training data creation and AI-based emotion recognition from user interaction data.</abstract>
    <parentTitle language="eng">MODELS Companion ´24</parentTitle>
    <identifier type="doi">10.1145/3652620.3686245</identifier>
    <enrichment key="ConferenceStatement">27th International Conferene on Model Driven Engineering Language and Systems</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Carina Bieber</author>
    <author>Patrick Harms</author>
    <author>Dominick Leppich</author>
    <author>Katrin Proschek</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data set, artificial intelligence, emotion recognition, user interaction</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="Forschungsschwerpunkt" number="5">Digitalisierung &amp; Künstliche Intelligenz</collection>
    <collection role="institutes" number="">Ohm User Experience Center (Ohm-UX)</collection>
  </doc>
  <doc>
    <id>3070</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>23</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume/>
    <type>article</type>
    <publisherName>Oldenburg Wissenschaftsverlag</publisherName>
    <publisherPlace>Oldenburg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-08-15</completedDate>
    <publishedDate>2023-08-15</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DUX : a dataset of user interactions and user emotions</title>
    <abstract language="eng">User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users’ emotions based on their interactions alone would not be intrusive to users and can be implemented in many applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.</abstract>
    <parentTitle language="deu">i-com : Journal of Interactive Media</parentTitle>
    <identifier type="doi">10.1515/icom-2023-0014</identifier>
    <identifier type="issn">2196-6826</identifier>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dominick Leppich</author>
    <author>Carina Bieber</author>
    <author>Katrin Proschek</author>
    <author>Patrick Harms</author>
    <author>Ulf Schubert</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>dataset</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>user emotions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>user experience</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>user interactions</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="institutes" number="">Ohm User Experience Center (Ohm-UX)</collection>
  </doc>
</export-example>
