@inproceedings{53816,
  abstract     = {{Augmented (AR) and Virtual Reality (VR) technologies have been applied very broadly in the recent past. While prior work emphasizes the potential of these technologies in various application domains, the process of visual attention in and across the contexts of AR/VR environments is not exhaustively explored yet. By now, visual attention in AR/VR environments has majorly been studied by means of overt attention (i.e. saccadic eye movements), self-report, and process-related visual attention proxies (like reaction time). In this work, we analyze covert visual attention based on the (psychological) Theory of Visual Attention (TVA), which allows us to quantify theory-based interpretable properties of the visual attention process. For example, the TVA allows us to measure the overall processing speed. We instantiate this TVA-based framework with a 30-participant explorative within-subjects study. The results show a decisive difference in visual attention between Reality (i.e. the neutral condition) and Virtual Reality and a weak difference between Reality and Augmented Reality. We discuss the consequences of our findings and provide ideas for future studies.}},
  author       = {{Biermeier, Kai and Scharlau, Ingrid and Yigitbas, Enes}},
  booktitle    = {{Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2024)}},
  keywords     = {{Visual Attention, TVA, Cognitive Modelling, Bayesian Modelling, AR, VR}},
  publisher    = {{ACM}},
  title        = {{{Measuring Visual Attention Capacity Across xReality}}},
  doi          = {{10.1145/3652037.3652050}},
  year         = {{2024}},
}

@inproceedings{33957,
  abstract     = {{Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.}},
  author       = {{Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}},
  keywords     = {{Assistance system, Knowledge graph, Information retrieval, Neural networks, AR}},
  location     = {{Stuttgart}},
  title        = {{{AI-Based Assistance System for Manufacturing}}},
  doi          = {{10.1109/ETFA52439.2022.9921520}},
  year         = {{2022}},
}

@inproceedings{33914,
  abstract     = {{Workshops on business model generation lead to collaborative work phases and discussions on business models. Therefore, tools such as the Business Model Canvas are used, typically filled with sticky notes. Generated content needs to be digitized in a time-consuming manual follow-up as part of the documentation and basis for a further use of the results in the company. In addition, there are challenges, such as decentralized work and digital workshop formats. Augmented Reality offers a way to reduce the digitization effort and enables decentralized work. In this research, the potentials of the use of AR technology in workshops on business model generation is investigated. Therefore, functions are implemented and evaluated in a demonstrator that reduces digitization effort and enable distributed work.}},
  author       = {{Gräßler, Iris and Grewe, Benedikt and Kramer, Hendrik and Pottebaum, Jens}},
  booktitle    = {{LUT Scientific and Expertise Publications}},
  keywords     = {{business model generation, augmented reality, workshop, collaborative work, digitization, AR-supported workshop concept, immersive technologies, decentralized work, business model canvas}},
  location     = {{Copenhagen}},
  title        = {{{Supporting Business Model Generation with Augmented Reality}}},
  year         = {{2022}},
}