Jannik Ottmann (formerly Franssen) received his B.Sc. in Computer Science and M.Eng. in Industrial Informatics from the University of Applied Sciences Emden/Leer, specializing in Human-Computer Interaction (HCI), Mixed Reality (AR/VR), IoT, and robotics. During his studies, he worked as a student assistant in the research projects Heb@AR and XR4HealthCare, contributing to the Heb@AR App, which received the AVRiL Gold and nextReality Interactive Education awards in 2022. His master’s thesis explored the integration of Large Language Models (LLMs) into procedural XR trainings through context-aware conversational assistance.

In May 2024, he joined the Mixality Group as a Research Associate in the TwinMaP project, developing adaptive worker assistance systems combining web-based 3D visualization, digital twins, IoT, and VR. Alongside his research, he managed the technical infrastructure of the Mixality Lab. Since October 2026, he has been working on the Zukunftscampus project at the Virtual University of Applied Sciences (VFH).

Publications

• F. Krumstroh, A. Eisenmann, J. Franssen, M. Rindermann, and T. Pfeiffer, “Comparing Fiducial Marker Tracking Across Cameras in Virtual and Physical Environments,” in GI Workshop 2025 on Virtual and Augmented Reality, 2025. doi:10.18420/vrar2025_08
  [BibTeX] [Abstract] [Download PDF]

  Robust object tracking is a foundational technology for augmented and virtual reality (AR/VR) systems. While many benchmarking efforts rely solely on simulated environments, these often lack real-world fidelity, limiting practical insights. To bridge this gap, we propose a hybrid evaluation approach, combining a physical test rig—constructed from aluminum extrusion frames and ArUco markers—with a digital twin created in Blender. Our approach facilitates direct sim-to-real comparisons of fiducial marker tracking performance. We benchmark the widely used OpenCV ArUco (Augmented Reality library from the University of Córdoba) against a custom-optimized tracking approach, BART (Bielefeld Augmented Reality Tracker), a modern performance-optimized alternative, to evaluate whether tracking limitations stem from the ArUco implementation itself or are inherent to fiducial marker tracking in general. Our evaluation focuses on detection accuracy and pose estimation performance. The results offer practical insight into the differences between tracking performance in real and simulated environments.

  @inproceedings{Krumstroh_2025,
  author = {Krumstroh, Felix and Eisenmann, Andrés and Franssen, Jannik and Rindermann, Moritz and Pfeiffer, Thies},
  title = {Comparing Fiducial Marker Tracking Across Cameras in Virtual and Physical Environments},
  year = 2025,
  doi = {10.18420/vrar2025_08},
  booktitle = {GI Workshop 2025 on Virtual and Augmented Reality},
  publisher = {Gesellschaft für Informatik, Bonn},
  url = {https://mixality.de/wp-content/uploads/2026/01/Krumstroh2025.pdf},
  issn = {2944-7682},
  abstract = {Robust object tracking is a foundational technology for augmented and virtual reality (AR/VR) systems. While many benchmarking efforts rely solely on simulated environments, these often lack real-world fidelity, limiting practical insights. To bridge this gap, we propose a hybrid evaluation approach, combining a physical test rig—constructed from aluminum extrusion frames and ArUco markers—with a digital twin created in Blender. Our approach facilitates direct sim-to-real comparisons of fiducial marker tracking performance. We benchmark the widely used OpenCV ArUco (Augmented Reality library from the University of Córdoba) against a custom-optimized tracking approach, BART (Bielefeld Augmented Reality Tracker), a modern performance-optimized alternative, to evaluate whether tracking limitations stem from the ArUco implementation itself or are inherent to fiducial marker tracking in general. Our evaluation focuses on detection accuracy and pose estimation performance. The results offer practical insight into the differences between tracking performance in real and simulated environments.}
  }

• A. Dirks, J. Franssen, and T. Pfeiffer, “Towards Computational Quality Management for Automatically Generated Adaptive Work Instructions in Industry 5.0,” in 2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS), 2025, pp. 1-6. doi:10.1109/ICPS65515.2025.11087874
  [BibTeX] [Abstract] [Download PDF]

  Industry 4.0 focused on digitalization to make machines work together effectively. Industry 5.0 will put more emphasis on the human worker, whose cognitive abilities and adaptability are invaluable to the factory of the future. We expect a lot from the Operator 5.0 in terms of skills and knowledge, hence research on cognitive assistance systems that can help manage the cognitive load by providing automated digital instructions and production information is highly relevant. To achieve a certain standard, we look at previous work on instruction quality and put it into perspective with insights from UX and instructional design. We argue, that many frameworks lack an objective measure to evaluate and monitor instruction quality. However, assistance systems in Industry 5.0 are designed to generate instructions automatically and adapt them to changes in real-time. Hence, the systems need a way to verify the quality of the produced instructions in order to work effectively and avoid errors. Focusing on assembly tasks, we first identify responsibilities for quality issues by assigning them to different components of an assistance system, before identifying initial objective metrics for the system to rely on.

  @inproceedings{11087874,
  author = {Dirks, André and Franssen, Jannik and Pfeiffer, Thies},
  booktitle = {2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS)},
  title = {Towards Computational Quality Management for Automatically Generated Adaptive Work Instructions in Industry 5.0},
  year = {2025},
  volume = {},
  number = {},
  pages = {1-6},
  keywords = {Measurement;Reviews;Linguistics;Real-time systems;Production facilities;Object recognition;Fifth Industrial Revolution;Monitoring;Standards;Quality management;Industry 5.0;work instructions;assistance systems;quality management},
  abstract = {Industry 4.0 focused on digitalization to make machines work together effectively. Industry 5.0 will put more emphasis on the human worker, whose cognitive abilities and adaptability are invaluable to the factory of the future. We expect a lot from the Operator 5.0 in terms of skills and knowledge, hence research on cognitive assistance systems that can help manage the cognitive load by providing automated digital instructions and production information is highly relevant. To achieve a certain standard, we look at previous work on instruction quality and put it into perspective with insights from UX and instructional design. We argue, that many frameworks lack an objective measure to evaluate and monitor instruction quality. However, assistance systems in Industry 5.0 are designed to generate instructions automatically and adapt them to changes in real-time. Hence, the systems need a way to verify the quality of the produced instructions in order to work effectively and avoid errors. Focusing on assembly tasks, we first identify responsibilities for quality issues by assigning them to different components of an assistance system, before identifying initial objective metrics for the system to rely on.},
  doi = {10.1109/ICPS65515.2025.11087874},
  url = {https://mixality.de/wp-content/uploads/2026/01/Dirks2025Towards.pdf}
  }

• J. Blattgerste, J. Franssen, M. Arztmann, and T. Pfeiffer, “Motivational benefits and usability of a handheld Augmented Reality game for anatomy learning,” in 2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), 2022.
  [BibTeX] [Download PDF]

  @inproceedings{Blattgerste2022Motivational,
  title = {Motivational benefits and usability of a handheld Augmented Reality game for anatomy learning},
  author = {Blattgerste, Jonas and Franssen, Jannik and Arztmann, Michaela and Pfeiffer, Thies},
  booktitle = {2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},
  year = {2022},
  organization = {IEEE},
  url = {https://mixality.de/wp-content/uploads/2022/12/Blattgerste2022Motivational.pdf}
  }

• J. Blattgerste, C. Lewa, K. Vogel, T. Willmeroth, S. Janßen, J. Franssen, J. Behrends, M. Joswig, T. Schäfer, N. H. Bauer, A. Bernloehr, and T. Pfeiffer, “Die Heb@AR App – Eine Android & iOS App mit Augmented Reality Trainings für selbstbestimmtes und curriculares Lernen in der hochschulischen Hebammenausbildung,” in Wettbewerbsband AVRiL 2022, Bonn, 2022, pp. 4-9. doi:10.18420/avril2022_01
  [BibTeX] [Download PDF]

  @inproceedings{Blattgerste2022HebARAVRiL,
  author = {Blattgerste, Jonas AND Lewa , Carmen AND Vogel, Kristina AND Willmeroth, Tabea AND Janßen, Sven AND Franssen, Jannik AND Behrends, Jan AND Joswig, Matthias AND Schäfer, Thorsten AND Bauer, Nicola H. AND Bernloehr, Annette AND Pfeiffer, Thies},
  title = {Die Heb@AR App - Eine Android & iOS App mit Augmented Reality Trainings für selbstbestimmtes und curriculares Lernen in der hochschulischen Hebammenausbildung},
  booktitle = {Wettbewerbsband AVRiL 2022},
  year = {2022},
  editor = {Söbke, Heinrich AND Zender, Raphael},
  pages = {4-9},
  doi = {10.18420/avril2022_01},
  publisher = {Gesellschaft für Informatik e.V.},
  address = {Bonn},
  url = {https://mixality.de/wp-content/uploads/2022/12/Blattgerste2022HebARAVRiL.pdf}
  }