2026
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
From Groups to Individuals: Generalization Challenges of HRV Based Classifiers Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@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}
}
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
On the Efficacy and Usability of Adaptive Instructional Systems Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@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}
}
Kretschmer-Trendowicz, Anett; Moser, Florian; Gürster, Lena; Pippirs, Corinna; Maas, Pia; Zeiler, Anne; Steininger, Melissa; Walk, Simon; von Bock, Christian; Krüger, Björn; Spittler, Thomas
Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI) Conference Forthcoming
European Congress of Psychiatry 2026, Forthcoming.
@conference{kretschmer2026a,
title = {Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI)},
author = {Anett Kretschmer-Trendowicz and Florian Moser and Lena Gürster and Corinna Pippirs and Pia Maas and Anne Zeiler and Melissa Steininger and Simon Walk and Christian von Bock and Björn Krüger and Thomas Spittler},
year = {2026},
date = {2026-04-01},
booktitle = {European Congress of Psychiatry 2026},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{steininger2026a,
title = {Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients},
author = {Melissa Steininger and Anna Jansen and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-31},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
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Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{jansen2026a,
title = {Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-30},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
2025
Bhatti, Faraz Ahmad; Riaz, Qaiser; Krüger, Björn
Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors Journal Article
In: IEEE Access, 2025.
@article{Bhatti2025,
title = {Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors},
author = {Faraz Ahmad Bhatti and Qaiser Riaz and Björn Krüger},
doi = {10.1109/ACCESS.2025.3641198},
year = {2025},
date = {2025-12-05},
urldate = {2025-12-02},
journal = {IEEE Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas
Assessing the impact of Virtual Reality, Augmented Reality, and Video Games on improving post-traumatic stress disorder symptoms: A systematic review and meta-analysis Journal Article Forthcoming
In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, Forthcoming.
@article{Goharinejad-2025,
title = {Assessing the impact of Virtual Reality, Augmented Reality, and Video Games on improving post-traumatic stress disorder symptoms: A systematic review and meta-analysis},
author = {Saeideh Goharinejad and Salime Goharinezhad and Khadijeh Moulaei and Björn Krüger and Thomas Spittler},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {INQUIRY: The Journal of Health Care Organization, Provision, and Financing},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
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Moontaha, Sidratul; Cavalier, Constanze; Esser, Birgitta; Jordan, Arthur; Goebel, Ines; Anders, Christoph; Mimi, Afsana; Krüger, Björn; Surges, Rainer; Arnrich, Bert
EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients Journal Article
In: Scientific Data, vol. 12, iss. 1, no. 1867, 2025, ISBN: 2052-4463.
@article{Moontaha2025,
title = {EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients},
author = {Sidratul Moontaha and Constanze Cavalier and Birgitta Esser and Arthur Jordan and Ines Goebel and Christoph Anders and Afsana Mimi and Björn Krüger and Rainer Surges and Bert Arnrich},
url = {https://doi.org/10.1038/s41597-025-06328-3},
doi = {10.1038/s41597-025-06328-3},
isbn = {2052-4463},
year = {2025},
date = {2025-11-28},
urldate = {2025-12-01},
journal = {Scientific Data},
volume = {12},
number = {1867},
issue = {1},
abstract = {Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events. Steininger, Melissa; Marquardt, Alexander; Perusquía-Hernández, Monica; Lehnort, Marvin; Otsubo, Hiromu; Dollack, Felix; Kruijff, Ernst; Krüger, Björn; Kiyokawa, Kiyoshi; Riecke, Bernhard E.
The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1267-1277, 2025.
@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}
}
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. Jansen, Anna; Morev, Nikita; Steininger, Melissa; Müllers, Johannes; Krüger, Björn
Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 809-810, IEEE Computer Society, 2025.
@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}
}
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. Alavi, Khashayar; Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{alavi2025a,
title = {Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy},
author = {Khashayar Alavi and Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
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Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Search Behavior – Metrics for Analysis of Eye Tracking Data Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{jansen2025b,
title = {Search Behavior – Metrics for Analysis of Eye Tracking Data},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
journal = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Pukropski, Jan; Weber, Christian; Müllers, Johannes; Grond, Martin; Surges, Rainer; Krüger, Björn
Implementation of a User-Friendly System in Epileptologic Teleconsultation Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{prukopski2025a,
title = {Implementation of a User-Friendly System in Epileptologic Teleconsultation},
author = {Jan Pukropski and Christian Weber and Johannes Müllers and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
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}
Mustafa, Sarah Al-Haj; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Surges, Rainer; von Wrede, Randi; Krüger, Björn; Helmstaedter, Christoph
Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy Journal Article
In: Epilepsy & Behavior, vol. 173, iss. December 2025, no. 110562, 2025.
@article{alhaj2025,
title = {Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Melissa Steininger and Johannes Müllers and Rainer Surges and Randi von Wrede and Björn Krüger and Christoph Helmstaedter},
doi = {10.1016/j.yebeh.2025.110562},
year = {2025},
date = {2025-06-30},
urldate = {2025-06-30},
journal = {Epilepsy & Behavior},
volume = {173},
number = {110562},
issue = {December 2025},
abstract = {Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy. Greß, Hannah; Demidova, Elena; Meier, Michael; Krüger, Björn
SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events Proceedings Article
In: Ohm, Marc (Ed.): Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025), pp. 22-24, GI SIG SIDAR, Nuremberg, April, 2025, ISSN: 2190-846X.
@inproceedings{Greß2025,
title = {SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events},
author = {Hannah Greß and Elena Demidova and Michael Meier and Björn Krüger},
editor = {Marc Ohm},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
issn = {2190-846X},
year = {2025},
date = {2025-05-12},
urldate = {2025-05-12},
booktitle = { Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025)},
pages = {22-24},
publisher = {GI SIG SIDAR},
address = {Nuremberg, April},
abstract = {In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users. Khan, Umar; Riaz, Qaiser; Hussain, Mehdi; Zeeshan, Muhammad; Krüger, Björn
Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors Journal Article
In: Algorithms, vol. 18, no. 4, 2025, ISSN: 1999-4893.
@article{2025-khan,
title = {Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors},
author = {Umar Khan and Qaiser Riaz and Mehdi Hussain and Muhammad Zeeshan and Björn Krüger},
url = {https://www.mdpi.com/1999-4893/18/4/203},
doi = {10.3390/a18040203},
issn = {1999-4893},
year = {2025},
date = {2025-04-04},
urldate = {2025-01-01},
journal = {Algorithms},
volume = {18},
number = {4},
abstract = {Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification. Greß, Hannah; Alouardani, Saied; Hoffmann, Nico; Trebing, Pia; Becker, Albert J.; Surges, Rainer; Pitsch, Julika; Krüger, Björn
Digitale Transformation des Blutprobenmanagements Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{gress2025a,
title = {Digitale Transformation des Blutprobenmanagements},
author = {Hannah Greß and Saied Alouardani and Nico Hoffmann and Pia Trebing and Albert J. Becker and Rainer Surges and Julika Pitsch and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Haaga, Lisa; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Bausch, Marcel; Jordan, Arthur; Surges, Rainer; Krüger, Björn
EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{haaga2025a,
title = {EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien},
author = {Lisa Haaga and Anna Jansen and Melissa Steininger and Johannes Müllers and Marcel Bausch and Arthur Jordan and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
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}
Müllers, Johannes; Greß, Hannah; Weber, Christian; Nadeem, Mubaris; Hütwohl, Daniela; Pukropski, Jan; Grond, Martin; Surges, Rainer; Krüger, Björn
Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{muellers2025a,
title = {Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn},
author = {Johannes Müllers and Hannah Greß and Christian Weber and Mubaris Nadeem and Daniela Hütwohl and Jan Pukropski and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
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Mustafa, Sarah Al-Haj; Jansen, Anna; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{nokey,
title = {ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Neuß, Maike Susanne; Pitsch, Julika; Krüger, Björn; Becker, Albert J.; Surges, Rainer; Baumgartner, Tobias
Semiologische Charakteristika der Temporallappenepilepsie mit Antikörpern gegen GAD65 Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{neuss2025,
title = {Semiologische Charakteristika der Temporallappenepilepsie mit Antikörpern gegen GAD65},
author = {Maike Susanne Neuß and Julika Pitsch and Björn Krüger and Albert J. Becker and Rainer Surges and Tobias Baumgartner},
year = {2025},
date = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Greß, Hannah; Krüger, Björn; Tischhauser, Elmar
The Newer, the More Secure? Standards-Compliant Bluetooth Low Energy Man-in-the-Middle Attacks on Fitness Trackers Journal Article
In: Sensors, vol. 25, no. 6, 2025, ISSN: 1424-8220.
@article{2025gressBT,
title = {The Newer, the More Secure? Standards-Compliant Bluetooth Low Energy Man-in-the-Middle Attacks on Fitness Trackers},
author = {Hannah Greß and Björn Krüger and Elmar Tischhauser},
url = {https://www.mdpi.com/1424-8220/25/6/1815},
doi = {10.3390/s25061815},
issn = {1424-8220},
year = {2025},
date = {2025-03-14},
urldate = {2025-01-01},
journal = {Sensors},
volume = {25},
number = {6},
abstract = {The trend in self-tracking devices has remained unabated for years. Even if they record a large quantity of sensitive data, most users are not concerned about their data being transmitted and stored in a secure way from the device via the companion app to the vendor’s server. However, the secure implementation of this chain from the manufacturer is not always given, as various publications have already shown. Therefore, we first provide an overview of attack vectors within the ecosystem of self-tracking devices. Second, we evaluate the data security of eight contemporary fitness trackers from leading vendors by applying four still partly standards-compliant Bluetooth Low-Energy Man-in-the-Middle (MitM) attacks. Our results show that the examined devices are partially vulnerable against the attacks. For most of the trackers, the manufacturers put different security measures in place. These include short and user-initiated visibility and connectivity or app-level authentication to limit the attack surface. Interestingly, newer models are more likely to be attackable, underlining the constant need for verifying the security of BLE devices, reporting found vulnerabilities, and also strengthening standards and improving security awareness among manufacturers and users. Therefore, we finish our work with recommendations and best practices for law- and regulation-makers, vendors, and users on how to strengthen the security of BLE devices.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The trend in self-tracking devices has remained unabated for years. Even if they record a large quantity of sensitive data, most users are not concerned about their data being transmitted and stored in a secure way from the device via the companion app to the vendor’s server. However, the secure implementation of this chain from the manufacturer is not always given, as various publications have already shown. Therefore, we first provide an overview of attack vectors within the ecosystem of self-tracking devices. Second, we evaluate the data security of eight contemporary fitness trackers from leading vendors by applying four still partly standards-compliant Bluetooth Low-Energy Man-in-the-Middle (MitM) attacks. Our results show that the examined devices are partially vulnerable against the attacks. For most of the trackers, the manufacturers put different security measures in place. These include short and user-initiated visibility and connectivity or app-level authentication to limit the attack surface. Interestingly, newer models are more likely to be attackable, underlining the constant need for verifying the security of BLE devices, reporting found vulnerabilities, and also strengthening standards and improving security awareness among manufacturers and users. Therefore, we finish our work with recommendations and best practices for law- and regulation-makers, vendors, and users on how to strengthen the security of BLE devices. Steininger, Melissa; Jansen, Anna; Welle, Kristian; Krüger, Björn
Optimized Sensor Position Detection: Improving Visual Sensor Setups for Hand Tracking in VR Proceedings Article
In: 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 1388-1389, 2025.
@inproceedings{steininger2025b,
title = {Optimized Sensor Position Detection: Improving Visual Sensor Setups for Hand Tracking in VR},
author = {Melissa Steininger and Anna Jansen and Kristian Welle and Björn Krüger},
url = {https://ieeexplore.ieee.org/document/10972713},
doi = {10.1109/VRW66409.2025.00340},
year = {2025},
date = {2025-03-12},
urldate = {2025-03-12},
booktitle = {2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
pages = {1388-1389},
abstract = {Hand tracking plays an important role in many Virtual Reality (VR) applications, enabling natural user interactions. Achieving precise tracking is often challenged by occlusion and suboptimal sensor placement. To address these challenges, we developed the Sensor Positioning Simulator, a versatile tool designed to optimize sensor placement. To demonstrate its utility, we simulated scenes from the VIRTOSHA project, a VR-based surgical training platform. Evaluations show that the tool effectively positioned sensors to achieve maximum hand surface visibility and full hand movement area coverage, even in occlusion-heavy environments. Future developments include support for animated simulations and validation through real-world experiments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hand tracking plays an important role in many Virtual Reality (VR) applications, enabling natural user interactions. Achieving precise tracking is often challenged by occlusion and suboptimal sensor placement. To address these challenges, we developed the Sensor Positioning Simulator, a versatile tool designed to optimize sensor placement. To demonstrate its utility, we simulated scenes from the VIRTOSHA project, a VR-based surgical training platform. Evaluations show that the tool effectively positioned sensors to achieve maximum hand surface visibility and full hand movement area coverage, even in occlusion-heavy environments. Future developments include support for animated simulations and validation through real-world experiments. Müllers, Johannes; Staehle, Ricarda; Stroth, Sanna; Poustka, Luise; Krüger, Björn; Schulte-Rüther, Martin
Multi-Stream Analysis for Robust Head Gesture Classification in Natural Social Interaction: Leveraging High-Resolution Sensor Data for Optimized Visual Classification Conference
16. Wissenschaftlichen Tagung Autismus-Spektrum (WTAS), 2025.
@conference{muellers2025b,
title = {Multi-Stream Analysis for Robust Head Gesture Classification in Natural Social Interaction: Leveraging High-Resolution Sensor Data for Optimized Visual Classification},
author = {Johannes Müllers and Ricarda Staehle and Sanna Stroth and Luise Poustka and Björn Krüger and Martin Schulte-Rüther},
year = {2025},
date = {2025-03-07},
booktitle = {16. Wissenschaftlichen Tagung Autismus-Spektrum (WTAS)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Eye-Tracking Reveals Search Behaviour in Epilepsy Patients Conference
3rd International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, 2025.
@conference{steininger2025a,
title = {Eye-Tracking Reveals Search Behaviour in Epilepsy Patients},
author = {Melissa Steininger and Anna Jansen and Sarah Al-Haj Mustafa and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger
},
year = {2025},
date = {2025-03-03},
urldate = {2025-03-03},
booktitle = {3rd International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
journal = {3rd International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
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}
2026
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
From Groups to Individuals: Generalization Challenges of HRV Based Classifiers Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@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}
}
Gfesser, Torsten; Witte, Thomas; Krüger, Björn
On the Efficacy and Usability of Adaptive Instructional Systems Proceedings Article Forthcoming
In: HCI International 2026, Springer, Forthcoming.
@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}
}
Kretschmer-Trendowicz, Anett; Moser, Florian; Gürster, Lena; Pippirs, Corinna; Maas, Pia; Zeiler, Anne; Steininger, Melissa; Walk, Simon; von Bock, Christian; Krüger, Björn; Spittler, Thomas
Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI) Conference Forthcoming
European Congress of Psychiatry 2026, Forthcoming.
@conference{kretschmer2026a,
title = {Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI)},
author = {Anett Kretschmer-Trendowicz and Florian Moser and Lena Gürster and Corinna Pippirs and Pia Maas and Anne Zeiler and Melissa Steininger and Simon Walk and Christian von Bock and Björn Krüger and Thomas Spittler},
year = {2026},
date = {2026-04-01},
booktitle = {European Congress of Psychiatry 2026},
keywords = {},
pubstate = {forthcoming},
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Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{steininger2026a,
title = {Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients},
author = {Melissa Steininger and Anna Jansen and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-31},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
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Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls Conference Forthcoming
4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.
@conference{jansen2026a,
title = {Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Nataly Bouzan and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2026},
date = {2026-03-30},
booktitle = {4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {forthcoming},
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}
2025
Bhatti, Faraz Ahmad; Riaz, Qaiser; Krüger, Björn
Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors Journal Article
In: IEEE Access, 2025.
@article{Bhatti2025,
title = {Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection with Wearable Inertial Sensors},
author = {Faraz Ahmad Bhatti and Qaiser Riaz and Björn Krüger},
doi = {10.1109/ACCESS.2025.3641198},
year = {2025},
date = {2025-12-05},
urldate = {2025-12-02},
journal = {IEEE Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas
Assessing the impact of Virtual Reality, Augmented Reality, and Video Games on improving post-traumatic stress disorder symptoms: A systematic review and meta-analysis Journal Article Forthcoming
In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, Forthcoming.
@article{Goharinejad-2025,
title = {Assessing the impact of Virtual Reality, Augmented Reality, and Video Games on improving post-traumatic stress disorder symptoms: A systematic review and meta-analysis},
author = {Saeideh Goharinejad and Salime Goharinezhad and Khadijeh Moulaei and Björn Krüger and Thomas Spittler},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {INQUIRY: The Journal of Health Care Organization, Provision, and Financing},
keywords = {},
pubstate = {forthcoming},
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Moontaha, Sidratul; Cavalier, Constanze; Esser, Birgitta; Jordan, Arthur; Goebel, Ines; Anders, Christoph; Mimi, Afsana; Krüger, Björn; Surges, Rainer; Arnrich, Bert
EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients Journal Article
In: Scientific Data, vol. 12, iss. 1, no. 1867, 2025, ISBN: 2052-4463.
@article{Moontaha2025,
title = {EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients},
author = {Sidratul Moontaha and Constanze Cavalier and Birgitta Esser and Arthur Jordan and Ines Goebel and Christoph Anders and Afsana Mimi and Björn Krüger and Rainer Surges and Bert Arnrich},
url = {https://doi.org/10.1038/s41597-025-06328-3},
doi = {10.1038/s41597-025-06328-3},
isbn = {2052-4463},
year = {2025},
date = {2025-11-28},
urldate = {2025-12-01},
journal = {Scientific Data},
volume = {12},
number = {1867},
issue = {1},
abstract = {Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Epilepsy patients commonly report stress as a frequent seizure trigger; however, the objective seizure-stress relationship is unclear due to self-report biases and difficulty in objective quantification of stress. This work presents a dataset from twenty epilepsy patients undergoing cognitive stress elicitation protocols, participating in laboratory experiments with computer-based tasks at predefined difficulty levels, and in situational experiments by independently choosing tasks with at least two difficulty levels. Physiological signals from wearable electroencephalography, photoplethysmography, acceleration, electrodermal activity, and temperature sensors were recorded. The task-related perceived cognitive stress was collected using two 5-point Likert scales of self-reported mental workload and stress, contrasted by a pairwise NASA-TLX questionnaire. Additionally, the dataset includes a patient-reported list of seizure-provoking and -inhibiting factors. Results illustrated individual and heterogeneous responses to cognitive tasks, with some modalities yielding statistically significant features, while others demonstrated expected directional trends. The findings support the validity and suitability of the proposed dataset for cognitive stress detection and the potential to map seizure-related factors to cognitive stress events.
Steininger, Melissa; Marquardt, Alexander; Perusquía-Hernández, Monica; Lehnort, Marvin; Otsubo, Hiromu; Dollack, Felix; Kruijff, Ernst; Krüger, Björn; Kiyokawa, Kiyoshi; Riecke, Bernhard E.
The Awe-some Spectrum: Self-Reported Awe Varies by Eliciting Scenery and Presence in Virtual Reality, and the User's Nationality Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1267-1277, 2025.
@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}
}
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.
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.
Jansen, Anna; Morev, Nikita; Steininger, Melissa; Müllers, Johannes; Krüger, Björn
Synthetic Hand Dataset Generation: Multi-View Rendering and Annotation with Blender Proceedings Article
In: 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 809-810, IEEE Computer Society, 2025.
@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}
}
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.
Alavi, Khashayar; Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{alavi2025a,
title = {Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy},
author = {Khashayar Alavi and Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Search Behavior – Metrics for Analysis of Eye Tracking Data Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{jansen2025b,
title = {Search Behavior – Metrics for Analysis of Eye Tracking Data},
author = {Anna Jansen and Melissa Steininger and Sarah Al-Haj Mustafa and Johannes Müllers and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
journal = {International Congress on Mobile Health and Digital Technology in Epilepsy},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Pukropski, Jan; Weber, Christian; Müllers, Johannes; Grond, Martin; Surges, Rainer; Krüger, Björn
Implementation of a User-Friendly System in Epileptologic Teleconsultation Conference
International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.
@conference{prukopski2025a,
title = {Implementation of a User-Friendly System in Epileptologic Teleconsultation},
author = {Jan Pukropski and Christian Weber and Johannes Müllers and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-09-04},
urldate = {2025-09-04},
booktitle = {International Congress on Mobile Health and Digital Technology in Epilepsy},
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Mustafa, Sarah Al-Haj; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Surges, Rainer; von Wrede, Randi; Krüger, Björn; Helmstaedter, Christoph
Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy Journal Article
In: Epilepsy & Behavior, vol. 173, iss. December 2025, no. 110562, 2025.
@article{alhaj2025,
title = {Eyes on Cognition: Exploring Oculomotor Correlates of Cognitive Function in Patients with Epilepsy},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Melissa Steininger and Johannes Müllers and Rainer Surges and Randi von Wrede and Björn Krüger and Christoph Helmstaedter},
doi = {10.1016/j.yebeh.2025.110562},
year = {2025},
date = {2025-06-30},
urldate = {2025-06-30},
journal = {Epilepsy & Behavior},
volume = {173},
number = {110562},
issue = {December 2025},
abstract = {Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.
This study investigates the relationship between eye tracking parameters and cognitive performance during the Trail Making Test (TMT) in individuals with epilepsy and healthy controls. By analyzing ocular behaviors such as saccade velocity, fixation duration, and pupil diameter, we aim to determine how these metrics reflect executive functioning and attentional control.
Methods
A sample of 95 participants with epilepsy and 34 healthy controls completed the TMT while their eye movements were recorded. Partial correlations, controlling for age, sex, education, medication count, seizure status and epilepsy duration, examined associations between eye tracking measures and cognitive performance derived from EpiTrack and TMT performance.
Results
In the patient group, faster TMT-A performance was associated with shorter fix- ation durations (r = 0.31, p = 0.006). Lower minimum saccade velocity correlated with slower performance on both TMT-A (r = −0.35, p = 0.002) and TMT-B (r = −0.40, p<0.001), whereas higher peak saccade velocities were linked to worse performance (TMT-A: r = 0.45, p<0.001; TMT-B: r = 0.41, p<0.001). Pupil diameter findings indicated that slower TMT performance was associated with smaller minimum pupil sizes (r = −0.23 to r = −0.36), wich may indicate increased cognitive effort and attentional load. Higher EpiTrack scores also correlated with a smaller minimum pupil diameter − but only during the more demanding TMT-B − and with a more restricted saccade velocity range, reflecting greater motor control and attentional stability. No significant correlations emerged within the control group.
Conclusion
These findings highlight the potential of eye tracking as a non-invasive tool for assessing cognitive function in epilepsy. Efficient cognitive performance was characterized by stable and controlled eye movements, whereas impaired performance involved erratic saccade dynamics and prolonged fixations. Importantly, eye tracking parameters provide additional information beyond simple speed measurements, potentially enhancing the differential diagnostic capabilities of the TMT in epilepsy. The observed associations between oculomotor parameters and cognitive performance were not present in the control group, suggesting that these relationships may be specific to epilepsy. Future research should investigate whether both basic and advanced metrics of search strategies are sensitive to disease dynamics and treatment effects in epilepsy.
Greß, Hannah; Demidova, Elena; Meier, Michael; Krüger, Björn
SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events Proceedings Article
In: Ohm, Marc (Ed.): Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025), pp. 22-24, GI SIG SIDAR, Nuremberg, April, 2025, ISSN: 2190-846X.
@inproceedings{Greß2025,
title = {SecureNeuroAI: Advanced Security Framework for AI-Powered Multimodal Real-Time Detection of Medical Seizure Events},
author = {Hannah Greß and Elena Demidova and Michael Meier and Björn Krüger},
editor = {Marc Ohm},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
issn = {2190-846X},
year = {2025},
date = {2025-05-12},
urldate = {2025-05-12},
booktitle = { Proceedings of the 15th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2025)},
pages = {22-24},
publisher = {GI SIG SIDAR},
address = {Nuremberg, April},
abstract = {In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's interconnected world, medical devices are increasingly equipped with novel digital technologies and AI-powered methods to improve the users' quality of life.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.
Despite the increased possibilities and features these devices offer due to the technical progress, cyberattacks on medical devices will increase as well with possibly severe outcomes for the patients.
At the same time, AI-based technologies could help to detect and mitigate these attacks on medical systems and their data in real-time.
Therefore, our project "SecureNeuroAI" aims to detect epileptic seizures using multimodal sensor data and AI models while also considering possible cyberattacks on this system resulting in an IT-secure system.
Our results will serve as an example for future AI-supported medical devices and systems to enhance their security and to strengthen their trustworthiness towards their (future) users.
Khan, Umar; Riaz, Qaiser; Hussain, Mehdi; Zeeshan, Muhammad; Krüger, Björn
Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors Journal Article
In: Algorithms, vol. 18, no. 4, 2025, ISSN: 1999-4893.
@article{2025-khan,
title = {Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors},
author = {Umar Khan and Qaiser Riaz and Mehdi Hussain and Muhammad Zeeshan and Björn Krüger},
url = {https://www.mdpi.com/1999-4893/18/4/203},
doi = {10.3390/a18040203},
issn = {1999-4893},
year = {2025},
date = {2025-04-04},
urldate = {2025-01-01},
journal = {Algorithms},
volume = {18},
number = {4},
abstract = {Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification.
Greß, Hannah; Alouardani, Saied; Hoffmann, Nico; Trebing, Pia; Becker, Albert J.; Surges, Rainer; Pitsch, Julika; Krüger, Björn
Digitale Transformation des Blutprobenmanagements Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{gress2025a,
title = {Digitale Transformation des Blutprobenmanagements},
author = {Hannah Greß and Saied Alouardani and Nico Hoffmann and Pia Trebing and Albert J. Becker and Rainer Surges and Julika Pitsch and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Haaga, Lisa; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Bausch, Marcel; Jordan, Arthur; Surges, Rainer; Krüger, Björn
EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{haaga2025a,
title = {EpiEye – Einfluss anfallssupressiver Medikamente auf Augenbewegungen und autonome Veränderungen bei Epilepsien},
author = {Lisa Haaga and Anna Jansen and Melissa Steininger and Johannes Müllers and Marcel Bausch and Arthur Jordan and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
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Müllers, Johannes; Greß, Hannah; Weber, Christian; Nadeem, Mubaris; Hütwohl, Daniela; Pukropski, Jan; Grond, Martin; Surges, Rainer; Krüger, Björn
Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{muellers2025a,
title = {Aufbau eines epileptologischen Telekonsils zwischen dem Klinikum Siegen und dem Universitätsklinikum Bonn},
author = {Johannes Müllers and Hannah Greß and Christian Weber and Mubaris Nadeem and Daniela Hütwohl and Jan Pukropski and Martin Grond and Rainer Surges and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
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Mustafa, Sarah Al-Haj; Jansen, Anna; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{nokey,
title = {ANNE – Augen-Tracking zur Erkennung von Nebenwirkungen bei Epilepsie},
author = {Sarah Al-Haj Mustafa and Anna Jansen and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger},
year = {2025},
date = {2025-03-26},
urldate = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Neuß, Maike Susanne; Pitsch, Julika; Krüger, Björn; Becker, Albert J.; Surges, Rainer; Baumgartner, Tobias
Semiologische Charakteristika der Temporallappenepilepsie mit Antikörpern gegen GAD65 Conference
Dreiländertagung Epilepsie 2025, 2025.
@conference{neuss2025,
title = {Semiologische Charakteristika der Temporallappenepilepsie mit Antikörpern gegen GAD65},
author = {Maike Susanne Neuß and Julika Pitsch and Björn Krüger and Albert J. Becker and Rainer Surges and Tobias Baumgartner},
year = {2025},
date = {2025-03-26},
booktitle = {Dreiländertagung Epilepsie 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Greß, Hannah; Krüger, Björn; Tischhauser, Elmar
The Newer, the More Secure? Standards-Compliant Bluetooth Low Energy Man-in-the-Middle Attacks on Fitness Trackers Journal Article
In: Sensors, vol. 25, no. 6, 2025, ISSN: 1424-8220.
@article{2025gressBT,
title = {The Newer, the More Secure? Standards-Compliant Bluetooth Low Energy Man-in-the-Middle Attacks on Fitness Trackers},
author = {Hannah Greß and Björn Krüger and Elmar Tischhauser},
url = {https://www.mdpi.com/1424-8220/25/6/1815},
doi = {10.3390/s25061815},
issn = {1424-8220},
year = {2025},
date = {2025-03-14},
urldate = {2025-01-01},
journal = {Sensors},
volume = {25},
number = {6},
abstract = {The trend in self-tracking devices has remained unabated for years. Even if they record a large quantity of sensitive data, most users are not concerned about their data being transmitted and stored in a secure way from the device via the companion app to the vendor’s server. However, the secure implementation of this chain from the manufacturer is not always given, as various publications have already shown. Therefore, we first provide an overview of attack vectors within the ecosystem of self-tracking devices. Second, we evaluate the data security of eight contemporary fitness trackers from leading vendors by applying four still partly standards-compliant Bluetooth Low-Energy Man-in-the-Middle (MitM) attacks. Our results show that the examined devices are partially vulnerable against the attacks. For most of the trackers, the manufacturers put different security measures in place. These include short and user-initiated visibility and connectivity or app-level authentication to limit the attack surface. Interestingly, newer models are more likely to be attackable, underlining the constant need for verifying the security of BLE devices, reporting found vulnerabilities, and also strengthening standards and improving security awareness among manufacturers and users. Therefore, we finish our work with recommendations and best practices for law- and regulation-makers, vendors, and users on how to strengthen the security of BLE devices.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The trend in self-tracking devices has remained unabated for years. Even if they record a large quantity of sensitive data, most users are not concerned about their data being transmitted and stored in a secure way from the device via the companion app to the vendor’s server. However, the secure implementation of this chain from the manufacturer is not always given, as various publications have already shown. Therefore, we first provide an overview of attack vectors within the ecosystem of self-tracking devices. Second, we evaluate the data security of eight contemporary fitness trackers from leading vendors by applying four still partly standards-compliant Bluetooth Low-Energy Man-in-the-Middle (MitM) attacks. Our results show that the examined devices are partially vulnerable against the attacks. For most of the trackers, the manufacturers put different security measures in place. These include short and user-initiated visibility and connectivity or app-level authentication to limit the attack surface. Interestingly, newer models are more likely to be attackable, underlining the constant need for verifying the security of BLE devices, reporting found vulnerabilities, and also strengthening standards and improving security awareness among manufacturers and users. Therefore, we finish our work with recommendations and best practices for law- and regulation-makers, vendors, and users on how to strengthen the security of BLE devices.
Steininger, Melissa; Jansen, Anna; Welle, Kristian; Krüger, Björn
Optimized Sensor Position Detection: Improving Visual Sensor Setups for Hand Tracking in VR Proceedings Article
In: 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 1388-1389, 2025.
@inproceedings{steininger2025b,
title = {Optimized Sensor Position Detection: Improving Visual Sensor Setups for Hand Tracking in VR},
author = {Melissa Steininger and Anna Jansen and Kristian Welle and Björn Krüger},
url = {https://ieeexplore.ieee.org/document/10972713},
doi = {10.1109/VRW66409.2025.00340},
year = {2025},
date = {2025-03-12},
urldate = {2025-03-12},
booktitle = {2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
pages = {1388-1389},
abstract = {Hand tracking plays an important role in many Virtual Reality (VR) applications, enabling natural user interactions. Achieving precise tracking is often challenged by occlusion and suboptimal sensor placement. To address these challenges, we developed the Sensor Positioning Simulator, a versatile tool designed to optimize sensor placement. To demonstrate its utility, we simulated scenes from the VIRTOSHA project, a VR-based surgical training platform. Evaluations show that the tool effectively positioned sensors to achieve maximum hand surface visibility and full hand movement area coverage, even in occlusion-heavy environments. Future developments include support for animated simulations and validation through real-world experiments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hand tracking plays an important role in many Virtual Reality (VR) applications, enabling natural user interactions. Achieving precise tracking is often challenged by occlusion and suboptimal sensor placement. To address these challenges, we developed the Sensor Positioning Simulator, a versatile tool designed to optimize sensor placement. To demonstrate its utility, we simulated scenes from the VIRTOSHA project, a VR-based surgical training platform. Evaluations show that the tool effectively positioned sensors to achieve maximum hand surface visibility and full hand movement area coverage, even in occlusion-heavy environments. Future developments include support for animated simulations and validation through real-world experiments.
Müllers, Johannes; Staehle, Ricarda; Stroth, Sanna; Poustka, Luise; Krüger, Björn; Schulte-Rüther, Martin
Multi-Stream Analysis for Robust Head Gesture Classification in Natural Social Interaction: Leveraging High-Resolution Sensor Data for Optimized Visual Classification Conference
16. Wissenschaftlichen Tagung Autismus-Spektrum (WTAS), 2025.
@conference{muellers2025b,
title = {Multi-Stream Analysis for Robust Head Gesture Classification in Natural Social Interaction: Leveraging High-Resolution Sensor Data for Optimized Visual Classification},
author = {Johannes Müllers and Ricarda Staehle and Sanna Stroth and Luise Poustka and Björn Krüger and Martin Schulte-Rüther},
year = {2025},
date = {2025-03-07},
booktitle = {16. Wissenschaftlichen Tagung Autismus-Spektrum (WTAS)},
keywords = {},
pubstate = {published},
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}
Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn
Eye-Tracking Reveals Search Behaviour in Epilepsy Patients Conference
3rd International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, 2025.
@conference{steininger2025a,
title = {Eye-Tracking Reveals Search Behaviour in Epilepsy Patients},
author = {Melissa Steininger and Anna Jansen and Sarah Al-Haj Mustafa and Rainer Surges and Christoph Helmstaedter and Randi von Wrede and Björn Krüger
},
year = {2025},
date = {2025-03-03},
urldate = {2025-03-03},
booktitle = {3rd International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
journal = {3rd International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}