Digital Health Bonn

@inproceedings{gfesser2026a,

title = {From Groups to Individuals: Generalization Challenges of HRV Based Classifiers},

author = {Torsten Gfesser and Thomas Witte and Björn Krüger},

year  = {2026},

date = {2026-07-31},

urldate = {2026-07-31},

booktitle = {HCI International 2026},

publisher = {Springer},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {On the Efficacy and Usability of Adaptive Instructional Systems},

author = {Torsten Gfesser and Thomas Witte and Björn Krüger},

year  = {2026},

date = {2026-07-31},

urldate = {2026-07-31},

booktitle = {HCI International 2026},

publisher = {Springer},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{steininger2026b,

title = {Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test},

author = {Melissa Steininger and Anna Jansen and Johannes Müllers and Randi von Wrede and Björn Krüger},

year  = {2026},

date = {2026-03-31},

urldate = {2026-03-31},

booktitle = {IEEE VR 2026 Workshop: GEMINI},

abstract = {Cognitive screening is a routine component of epilepsy care. Established pen-and-paper instruments such as the Trail Making Test (TMT) primarily yield summary outcomes (e.g., completion time) that provide limited insight into visual search and executive-control processes affected by epilepsy-related brain network dysfunction. We present an eye-tracked Virtual Reality TMT (VR-TMT) as a controlled research instrument that enables process-level interpretable measurements. The system synchronizes continuous eye-movement streams with timestamped task events (task start/stop and node selections) and logs gaze-to-Area-of-Interest (AOI) intersections. To reduce VR-specific confounds that can compromise cognitive interpretation, we specify concrete design guidelines for 3D stimulus geometry and the VR+eye-tracking setup (e.g., viewing distance, field-of-view placement, target size).

In a feasibility pilot (n=8) usability ratings were favorable and cybersickness was low. Building on this foundation, we outline an analysis framework that derives contextualized gaze features and evaluates their added value in explaining established cognitive screening outcomes in epilepsy cohorts.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{jansen2026b,

title = {VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation},

author = {Anna Jansen and Kristoffer Waldow and Sebastian Pötter and Turhan Civelek and Melissa Steininger and Jerome Perret and Markus Wellmann and Steffen-Sascha Stein and David Lähner and Kristian Welle and Arnulph Fuhrmann and Björn Krüger},

year  = {2026},

date = {2026-03-31},

urldate = {2026-03-31},

booktitle = {IEEE VR 2026 Workshop: XR-MED},

abstract = {Osteosynthesis training requires development of force-sensitive manual skills and an understanding of workflows, which are difficult to acquire through theoretical instruction or cadaver-based training. While Virtual Reality (VR) offers new opportunities for surgical training, existing systems often focus on isolated subtasks, lacking integrated support for realistic interaction, procedural logic, and adaptability. This paper presents a work-in-progress VR training system designed for workflow-oriented osteosynthesis training. The system combines force feedback, physics-based tissue simulation and robust hand tracking in a modular architecture. Additionally, an expert-driven authoring workflow enables medical professionals to define and adapt training scenarios without programming.

Using a reference scenario for fibular fracture osteosynthesis, we describe the system design, core components, and current implementation status. We further discuss technical trade-offs, limitations, and directions for future validation. Our system establishes a foundation for force-sensitive, workflow-oriented VR training and serves as a basis for future studies in surgical education.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{steininger2025c,

title = {The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality},

author = {Melissa Steininger and Alexander Marquardt and Monica Perusquía-Hernández and Marvin Lehnort and Hiromu Otsubo and Felix Dollack and Ernst Kruijff and Björn Krüger and Kiyoshi Kiyokawa and Bernhard E. Riecke

},

doi = {10.1109/ISMAR67309.2025.00132},

year  = {2025},

date = {2025-11-11},

urldate = {2025-10-01},

booktitle = {2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)},

pages = {1267-1277},

abstract = {Awe is a multifaceted emotion often associated with the perception of vastness, that challenges existing mental frameworks. Despite its growing relevance in affective computing and psychological research, awe remains difficult to elicit and measure.

This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors.

For this study, we designed ten immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration data, and self-reported awe and presence from participants from Germany, Japan, and Jordan.

Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high self-reported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses.

These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context.

Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports—rather than physiological signals—remain the most consistent indicators of emotional impact.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jansen2025c,

title = {Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender},

author = {Anna Jansen and Nikita Morev and Melissa Steininger and Johannes Müllers and Björn Krüger},

url = {https://www.computer.org/csdl/proceedings-article/ismar-adjunct/2025/934700a809/2bKcNnpvzTG},

doi = {10.1109/ISMAR-Adjunct68609.2025.00201},

year  = {2025},

date = {2025-10-06},

urldate = {2025-10-06},

booktitle = {2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)},

pages = {809-810},

publisher = {IEEE Computer Society},

abstract = {Pose estimation is a common method for precise handtracking, which is important for natural interaction in virtual reality (VR). However, training those models requires large-scale datasets with accurate 3D annotations. Those are difficult to obtain due to the time-consuming data collection and the limited variety in captured scenarios. We present a work-in-progress Blender-based pipeline for generating synthetic multi-view hand datasets. Our system simulates Ultraleap Stereo IR 170-style images and extracts joint positions directly from a rigged hand model, eliminating the need for manual labeling or external tracking processes. The current pipeline version supports randomized static poses with per-frame annotations of joint positions, camera parameters, and rendered images. While extended hand variation, animation features, and different sensor-type simulations are still in progress, our pipeline already provides a flexible foundation for customizable dataset generation and reproducible hand-tracking model training.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}