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  <doc>
    <id>2991</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>234</pageFirst>
    <pageLast>239</pageLast>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications</title>
    <abstract language="eng">Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications.</abstract>
    <parentTitle language="eng">2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)</parentTitle>
    <identifier type="url">https://www.researchgate.net/profile/Rosilius_Maximilian/publication/355896115_Evaluation_of_Visual_Requirements_and_Software-Design_for_Immersive_Visibility_in_Industrial_Applications/links/6189827807be5f31b7591290/Evaluation-of-Visual-Requirements-and-Software-Design-for-Immersive-Visibility-in-Industrial-Applications.pdf</identifier>
    <enrichment key="opus.import.data">@inproceedingsrosilius2021evaluation, title=Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications, author=Rosilius, Maximilian and Wirsing, Benedikt and von Eitzen, Ingo and Wilhelm, Markus and Schmitt, Jan and Engelmann, Bastian and Bräutigam, Volker, booktitle=2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pages=234–239, year=2021, organization=IEEE</enrichment>
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    <enrichment key="opus.import.date">2023-06-13T12:32:02+00:00</enrichment>
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    <author>Maximilian Rosilius</author>
    <author>Benedikt Wirsing</author>
    <author>Ingo von Eitzen</author>
    <author>Markus Wilhelm</author>
    <author>Jan Schmitt</author>
    <author>Bastian Engelmann</author>
    <author>Volker Bräutigam</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
  </doc>
  <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>
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    <enrichment key="opus.import.date">2023-06-13T12:32:02+00:00</enrichment>
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    <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>
  <doc>
    <id>5757</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>18</issue>
    <volume>24</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control</title>
    <abstract language="eng">The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.</abstract>
    <parentTitle language="eng">sensors</parentTitle>
    <identifier type="doi">10.3390/s24185919</identifier>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-57575</identifier>
    <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>Aleksej Weber</author>
    <author>Markus Wilhelm</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <file>https://opus4.kobv.de/opus4-fhws/files/5757/Wilhelm_Analysis_Factors_Body_Tracking.pdf</file>
  </doc>
  <doc>
    <id>5603</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>2</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">Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis</title>
    <abstract language="eng">In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.</abstract>
    <parentTitle language="eng">buildings</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-56030</identifier>
    <identifier type="doi">10.3390/buildings14020340</identifier>
    <identifier type="issn">2075-5309</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Markus Wilhelm</author>
    <author>Frank Lotter</author>
    <author>Christian Scherdel</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Architecture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Building and Construction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Civil and Structural Engineering</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <file>https://opus4.kobv.de/opus4-fhws/files/5603/Wilhelm_Advancing_Efficiency_Mineral.pdf</file>
  </doc>
</export-example>
