<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2231</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>110</pageFirst>
    <pageLast>127</pageLast>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>36</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards gestured-based technologies for human-centred smart factories</title>
    <abstract language="eng">Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or – control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator – ErgoTakt – derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers’ ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.</abstract>
    <parentTitle language="eng">International Journal of Computer Integrated Manufacturing</parentTitle>
    <identifier type="url">https://doi.org/10.1080/0951192X.2022.2121424</identifier>
    <identifier type="issn">1362-3052</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International</licence>
    <author>Jan Schmitt</author>
    <author>Bastian Engelmann</author>
    <author>Vito Modesto Manghisi</author>
    <author>Markus Wilhelm</author>
    <author>Antonello Uva</author>
    <author>Michele Fiorentino</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gesture-based monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gesture-based control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>manufacturing</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="Regensburger_Klassifikation" number="ZG - ZS">Technik</collection>
    <collection role="ddc" number="670">Industrielle Fertigung</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/2231/Schmitt_Gesture-based_technologies.pdf</file>
  </doc>
  <doc>
    <id>2982</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>354</pageFirst>
    <pageLast>360</pageLast>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume>97</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ErgoTakt: A novel approach of human-centered balancing of manual assembly lines</title>
    <abstract language="eng">Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.</abstract>
    <parentTitle language="eng">Procedia CIRP</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.procir.2020.05.250</identifier>
    <enrichment key="opus.import.data">@articlewilhelm2021ergotakt, title=ErgoTakt: A novel approach of human-centered balancing of manual assembly lines, author=Wilhelm, Markus and Manghisi, Vito Modesto and Uva, Antonello and Fiorentino, Michele and Bräutigam, Volker and Engelmann, Bastian and Schmitt, Jan, journal=Procedia CIRP, volume=97, pages=354–360, year=2021, publisher=Elsevier</enrichment>
    <enrichment key="opus.import.dataHash">md5:95c22c02328024e57ed2834bf4846360</enrichment>
    <enrichment key="opus.import.date">2023-06-13T12:32:02+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpzI1Tx1</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">648861c2a3cdd4.16940320</enrichment>
    <author>Markus Wilhelm</author>
    <author>Vito Modesto Manghisi</author>
    <author>Antonello Uva</author>
    <author>Michele Fiorentino</author>
    <author>Volker Bräutigam</author>
    <author>Bastian Engelmann</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
  </doc>
</export-example>
