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}
}
Greß, Hannah; Dahl, Alanis; Tran, Mindy; Popovski, Marija Turkovic; Krüger, Björn
Theory vs. Practice: How Secure are Bluetooth Low Energy-Capable Health Devices Compared to Legal Requirements? Proceedings Article
In: Proceedings of the 16th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2026), pp. 46-48, German Informatics Society (GI), 2026, ISBN: ISSN 2190-846X.
@inproceedings{gress2026b,
title = {Theory vs. Practice: How Secure are Bluetooth Low Energy-Capable Health Devices Compared to Legal Requirements?},
author = {Hannah Greß and Alanis Dahl and Mindy Tran and Marija Turkovic Popovski and Björn Krüger
},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
isbn = {ISSN 2190-846X},
year = {2026},
date = {2026-05-11},
urldate = {2026-05-11},
booktitle = {Proceedings of the 16th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2026)},
pages = {46-48},
publisher = {German Informatics Society (GI)},
abstract = {In a world becoming increasingly ’smart’, be it in industry, at home, or in medicine, secure data storage and transmission become even more important. But how secure is this in practice, and do vendors comply with current law to adequately protect the users’ or companies’ data? We chose empatica’s EpiMonitor as a ’smart’ and medically certified device to exemplify what such a technical assessment and legal analysis could look and which vulnerabilities may occur. This assessment and analysis are transferable to other wearable devices, possibly with slight modifications.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In a world becoming increasingly ’smart’, be it in industry, at home, or in medicine, secure data storage and transmission become even more important. But how secure is this in practice, and do vendors comply with current law to adequately protect the users’ or companies’ data? We chose empatica’s EpiMonitor as a ’smart’ and medically certified device to exemplify what such a technical assessment and legal analysis could look and which vulnerabilities may occur. This assessment and analysis are transferable to other wearable devices, possibly with slight modifications. Greß, Hannah; Schreiter, Jonas; Rademacher, Michael; Krüger, Björn
BLE Security Testing: Survey of Attacks and Evaluation of Tools and Frameworks Journal Article
In: IEEE Open Journal of the Communications Society, 2026.
@article{gress2026c,
title = {BLE Security Testing: Survey of Attacks and Evaluation of Tools and Frameworks},
author = {Hannah Greß and Jonas Schreiter and Michael Rademacher and Björn Krüger },
doi = {10.1109/OJCOMS.2026.3691569},
year = {2026},
date = {2026-05-08},
urldate = {2026-05-08},
journal = {IEEE Open Journal of the Communications Society},
abstract = {Nowadays, more and more areas of our lives are becoming ‘smart,’ including homes, industrial
sites, and medical devices. Since sensitive data is transmitted over various protocols, manufacturers must
ensure that the data is adequately secured. To do so, penetration testing on these devices is an option,
ideally before market launch. Depending on the protocol, various tools and frameworks exist. We chose
the Bluetooth Low Energy (BLE) protocol for analysis due to its widespread use and started our work by
classifying possible threats to BLE into four categories based on the attacks executable by the identified
tools and frameworks. Threats targeting BLE Mesh and BLE privacy and localization were identified
through a literature review and added to the corresponding categories of our taxonomy. Subsequently, we
conducted an in-depth evaluation of these tools and frameworks targeting BLE (excluding BLE Mesh,
and BLE privacy and localization) on four medical devices to empirically determine which tools and
frameworks remain usable for pentesting BLE devices. Furthermore, we analyzed which threats in our
taxonomy remain feasible. Our results show that only eight out of 21 (38%) attacks can still be carried
out. The tools and frameworks capable of conducting these attacks are mostly still maintained, emphasizing
the importance of keeping such tools and frameworks up to date for future use. Nevertheless, current BLE
pentesting requires a patchwork of multiple, often unmaintained frameworks to realize all known attacks
— this demonstrates an urgent need for a single, extensible, actively maintained framework that allows
attacks to be integrated modularly.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nowadays, more and more areas of our lives are becoming ‘smart,’ including homes, industrial
sites, and medical devices. Since sensitive data is transmitted over various protocols, manufacturers must
ensure that the data is adequately secured. To do so, penetration testing on these devices is an option,
ideally before market launch. Depending on the protocol, various tools and frameworks exist. We chose
the Bluetooth Low Energy (BLE) protocol for analysis due to its widespread use and started our work by
classifying possible threats to BLE into four categories based on the attacks executable by the identified
tools and frameworks. Threats targeting BLE Mesh and BLE privacy and localization were identified
through a literature review and added to the corresponding categories of our taxonomy. Subsequently, we
conducted an in-depth evaluation of these tools and frameworks targeting BLE (excluding BLE Mesh,
and BLE privacy and localization) on four medical devices to empirically determine which tools and
frameworks remain usable for pentesting BLE devices. Furthermore, we analyzed which threats in our
taxonomy remain feasible. Our results show that only eight out of 21 (38%) attacks can still be carried
out. The tools and frameworks capable of conducting these attacks are mostly still maintained, emphasizing
the importance of keeping such tools and frameworks up to date for future use. Nevertheless, current BLE
pentesting requires a patchwork of multiple, often unmaintained frameworks to realize all known attacks
— this demonstrates an urgent need for a single, extensible, actively maintained framework that allows
attacks to be integrated modularly. Jansen, Anna; Waldow, Kristoffer; Pötter, Sebastian; Civelek, Turhan; Steininger, Melissa; Perret, Jerome; Wellmann, Markus; Stein, Steffen-Sascha; Lähner, David; Welle, Kristian; Fuhrmann, Arnulph; Krüger, Björn
VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation Proceedings Article
In: 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) , pp. 921-930, IEEE Computer Society, 2026.
@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},
url = {https://doi.ieeecomputersociety.org/10.1109/VRW70859.2026.00171},
doi = {10.1109/VRW70859.2026.00171},
year = {2026},
date = {2026-03-31},
urldate = {2026-03-31},
booktitle = {2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) },
pages = {921-930},
publisher = {IEEE Computer Society},
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 = {published},
tppubtype = {inproceedings}
}
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. Steininger, Melissa; Jansen, Anna; Müllers, Johannes; von Wrede, Randi; Krüger, Björn
Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test Proceedings Article
In: IEEE VR 2026 Workshop: GEMINI, 2026.
@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},
url = {https://www.computer.org/csdl/proceedings-article/vrw/2026/052900a106/2gdqrMPo2s0},
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 = {published},
tppubtype = {inproceedings}
}
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. Imran, Hamza Ali; Riaz, Qaiser; Hamza, Kiran; Muhammad, Shaida; Krüger, Björn
From Steps to Sentiments: Cross-Domain Transfer Learning for Activity-Based Emotion Detection in Wearable IoT Systems Journal Article
In: IEEE Internet of Things Journal, pp. 1-1, 2026.
@article{Imran2026a,
title = {From Steps to Sentiments: Cross-Domain Transfer Learning for Activity-Based Emotion Detection in Wearable IoT Systems},
author = {Hamza Ali Imran and Qaiser Riaz and Kiran Hamza and Shaida Muhammad and Björn Krüger},
doi = {10.1109/JIOT.2026.3666469},
year = {2026},
date = {2026-02-20},
urldate = {2026-02-20},
journal = {IEEE Internet of Things Journal},
pages = {1-1},
abstract = {Context-aware, gait-based sentiment analysis and emotion perception is an emerging research area within Internet of Things (IoT), aiming to make smart systems more intuitive and responsive. Recognizing emotions from wearable inertial sensor data is challenging due to subtle and compound emotional cues, variability across individuals and contexts, and limited, imbalanced datasets. To address these challenges, we propose Jazbat-Net, a lightweight neural network that leverages Transfer Learning (TL). The model is first trained on a large-scale, publicly available multi-activity dataset collected using wearable inertial sensors, and then retrained on a multi-class emotion dataset, effectively transferring knowledge from the pretraining phase. We evaluate Jazbat-Net with and without TL, across both smartwatch and smartphone based data, and for input dimensions ranging from 1D to 6D. The best results are achieved when pretrained on smartphone-based activity data and retrained on smartphone-based emotion data using a 1D input size. The proposed model attains an average classification accuracy of 95%, with a precision score of 95%, a recall score of 97%, and an F1-score of 96%. Moreover, Jazbat-Net achieves a low theoretical time complexity and requires only ≈ 6.96 M Multiply–Accumulate Operations (MACs), which is about 95% fewer computations than the previous State-of-the-Art (SOTA) model. Its space complexity is also low, with a model size of only ≈ 110 KB and peak activation memory of ≈ 0.35 MB. On-device evaluation on a Xiaomi 13T smartphone demonstrates that Jazbat-Net achieves a median inference latency of only ≈ 90.96 ms with a TFLite 32-bit floating point precision (FP32) model size of just ≈ 0.158 MB, making it ≈ 20× smaller and ≈ 20% faster than the previous SOTA model while maintaining comparable accuracy.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Context-aware, gait-based sentiment analysis and emotion perception is an emerging research area within Internet of Things (IoT), aiming to make smart systems more intuitive and responsive. Recognizing emotions from wearable inertial sensor data is challenging due to subtle and compound emotional cues, variability across individuals and contexts, and limited, imbalanced datasets. To address these challenges, we propose Jazbat-Net, a lightweight neural network that leverages Transfer Learning (TL). The model is first trained on a large-scale, publicly available multi-activity dataset collected using wearable inertial sensors, and then retrained on a multi-class emotion dataset, effectively transferring knowledge from the pretraining phase. We evaluate Jazbat-Net with and without TL, across both smartwatch and smartphone based data, and for input dimensions ranging from 1D to 6D. The best results are achieved when pretrained on smartphone-based activity data and retrained on smartphone-based emotion data using a 1D input size. The proposed model attains an average classification accuracy of 95%, with a precision score of 95%, a recall score of 97%, and an F1-score of 96%. Moreover, Jazbat-Net achieves a low theoretical time complexity and requires only ≈ 6.96 M Multiply–Accumulate Operations (MACs), which is about 95% fewer computations than the previous State-of-the-Art (SOTA) model. Its space complexity is also low, with a model size of only ≈ 110 KB and peak activation memory of ≈ 0.35 MB. On-device evaluation on a Xiaomi 13T smartphone demonstrates that Jazbat-Net achieves a median inference latency of only ≈ 90.96 ms with a TFLite 32-bit floating point precision (FP32) model size of just ≈ 0.158 MB, making it ≈ 20× smaller and ≈ 20% faster than the previous SOTA model while maintaining comparable accuracy.
Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas
In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, vol. 63, pp. 00469580251413101, 2026.
@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},
url = {https://doi.org/10.1177/00469580251413101},
doi = {10.1177/00469580251413101},
year = {2026},
date = {2026-01-28},
urldate = {2025-12-01},
journal = {INQUIRY: The Journal of Health Care Organization, Provision, and Financing},
volume = {63},
pages = {00469580251413101},
abstract = {Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
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}
}
Barzegar, Mohammad Mehdi; Daryakenari, Nazila Ahmadi; Khodatars, Marjane
Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model Journal Article
In: Journal of Research and Health, vol. 15, no. 6, 2025.
@article{Barzegar2025,
title = {Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model},
author = {Mohammad Mehdi Barzegar and Nazila Ahmadi Daryakenari and Marjane Khodatars},
url = {http://jrh.gmu.ac.ir/article-1-2987-en.html},
doi = {10.32598/JRH.15.SP.2892.1},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Research and Health},
volume = {15},
number = {6},
abstract = {Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments. 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
},
url = {https://digital-health-bonn.de/wp-content/uploads/2026/05/2025_Steininger_Awe.pdf, Preprint version of the Paper},
doi = {10.1109/ISMAR67309.2025.00132},
year = {2025},
date = {2025-11-11},
urldate = {2025-11-11},
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. 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; 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.2024
Otsubo, Hiromu; Lehnort, Marvin; Steininger, Melissa; Marquardt, Alexander; Dollack, Felix; Hirao, Yutaro; Perusquía-Hernández, Monica; Uchiyama, Hideaki; Kruijff, Ernst; Riecke, Bernhard; Kiyokawa, Kiyoshi
First-Person Perspective Induces Stronger Feelings of Awe and Presence Compared to Third-Person Perspective in Virtual Reality Proceedings Article
In: ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction, pp. 439 - 448, 2024.
@inproceedings{Otsubo2024,
title = {First-Person Perspective Induces Stronger Feelings of Awe and Presence Compared to Third-Person Perspective in Virtual Reality},
author = {Hiromu Otsubo and Marvin Lehnort and Melissa Steininger and Alexander Marquardt and Felix Dollack and Yutaro Hirao and Monica Perusquía-Hernández and Hideaki Uchiyama and Ernst Kruijff and Bernhard Riecke and Kiyoshi Kiyokawa},
doi = {https://doi.org/10.1145/3678957.3685753},
year = {2024},
date = {2024-11-04},
urldate = {2024-11-04},
booktitle = {ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction},
pages = {439 - 448},
abstract = {Awe is a complex emotion described as a perception of vastness and a need for accommodation to integrate new, overwhelming experiences. Virtual Reality (VR) has recently gained attention as a convenient means to facilitate experiences of awe. In VR, a first-person perspective might increase awe due to its immersive nature, while a third-person perspective might enhance the perception of vastness. However, the impact of VR perspectives on experiencing awe has not been thoroughly examined. We created two types of VR scenes: one with elements designed to induce high awe, such as a snowy mountain, and a low awe scene without such elements. We compared first-person and third-person perspectives in each scene. Forty-two participants explored the VR scenes, with their physiological responses captured by electrocardiogram (ECG) and face tracking (FT). Subsequently, participants self-reported their experience of awe (AWE-S) and presence (IPQ) within VR. The results revealed that the first-person perspective induced stronger feelings of awe and presence than the third-person perspective. The findings of this study provide useful guidelines for designing VR content that enhances emotional experiences.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Awe is a complex emotion described as a perception of vastness and a need for accommodation to integrate new, overwhelming experiences. Virtual Reality (VR) has recently gained attention as a convenient means to facilitate experiences of awe. In VR, a first-person perspective might increase awe due to its immersive nature, while a third-person perspective might enhance the perception of vastness. However, the impact of VR perspectives on experiencing awe has not been thoroughly examined. We created two types of VR scenes: one with elements designed to induce high awe, such as a snowy mountain, and a low awe scene without such elements. We compared first-person and third-person perspectives in each scene. Forty-two participants explored the VR scenes, with their physiological responses captured by electrocardiogram (ECG) and face tracking (FT). Subsequently, participants self-reported their experience of awe (AWE-S) and presence (IPQ) within VR. The results revealed that the first-person perspective induced stronger feelings of awe and presence than the third-person perspective. The findings of this study provide useful guidelines for designing VR content that enhances emotional experiences. Marquardt, Alexander; Lehnort, Marvin; Otsubo, Hiromu; Perusquia-Hernandez, Monica; Steininger, Melissa; Dollack, Felix; Uchiyama, Hideaki; Kiyokawa, Kiyoshi; Kruijff, Ernst
Exploring Gesture Interaction in Underwater Virtual Reality Proceedings Article
In: Proceedings of the 2024 ACM Symposium on Spatial User Interaction, pp. 1-2, 2024.
@inproceedings{marquardtLehnort2024,
title = {Exploring Gesture Interaction in Underwater Virtual Reality},
author = {Alexander Marquardt and Marvin Lehnort and Hiromu Otsubo and Monica Perusquia-Hernandez and Melissa Steininger and Felix Dollack and Hideaki Uchiyama and Kiyoshi Kiyokawa and Ernst Kruijff},
doi = {https://doi.org/10.1145/3677386.3688890},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {Proceedings of the 2024 ACM Symposium on Spatial User Interaction},
pages = {1-2},
abstract = {An underwater virtual reality (UVR) system with gesture-based controls was developed to facilitate navigation and interaction while submerged. The system uses a waterproof head-mounted display and camera-based gesture recognition, originally trained for abovewater conditions, employing three gestures: grab for navigation, pinch for single interactions, and point for continuous interactions. In an experimental study, we tested gesture recognition both above and underwater, and evaluated participant interaction within an immersive underwater scene. Results showed that underwater conditions slightly affected gesture accuracy, but the system maintained high performance. Participants reported a strong sense of presence and found the gestures intuitive while highlighting the need for further refinement to address usability challenges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
An underwater virtual reality (UVR) system with gesture-based controls was developed to facilitate navigation and interaction while submerged. The system uses a waterproof head-mounted display and camera-based gesture recognition, originally trained for abovewater conditions, employing three gestures: grab for navigation, pinch for single interactions, and point for continuous interactions. In an experimental study, we tested gesture recognition both above and underwater, and evaluated participant interaction within an immersive underwater scene. Results showed that underwater conditions slightly affected gesture accuracy, but the system maintained high performance. Participants reported a strong sense of presence and found the gestures intuitive while highlighting the need for further refinement to address usability challenges. Kiran, Samia; Riaz, Qaiser; Hussain, Mehdi; Zeeshan, Muhammad; Krüger, Björn
Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes Journal Article
In: IEEE Sensors Journal, vol. 24, no. 15, pp. 24086-24095, 2024, ISSN: 1558-1748.
@article{10552639,
title = {Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes},
author = {Samia Kiran and Qaiser Riaz and Mehdi Hussain and Muhammad Zeeshan and Björn Krüger},
doi = {10.1109/JSEN.2024.3407835},
issn = {1558-1748},
year = {2024},
date = {2024-08-01},
urldate = {2024-01-01},
journal = {IEEE Sensors Journal},
volume = {24},
number = {15},
pages = {24086-24095},
abstract = {Falling poses a significant challenge to the health and well-being of the elderly and people with various disabilities. Precise and prompt fall detection plays a crucial role in preventing falls and mitigating the impact of injuries. In this research, we propose a deep classifier for pre-impact fall detection which can detect a fall in the pre-impact phase with an inference time of 46–52 milliseconds. The proposed classifier is an ensemble of Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRU) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available preimpact fall dataset. The dataset covers 36 diverse activities, including 15 types of fall-related activities and 21 types of activities of daily living (ADLs). Furthermore, we evaluated the proposed model using three different inputs of varying dimensions: 6D input (comprising 3D accelerations and 3D angular velocities), 3D input (3D accelerations), and 1D input (magnitude of 3D accelerations). The reduction in the input space from 6D to 1D is aimed at minimizing the computation cost. We have attained commendable results outperforming the state-of-the-art approaches by achieving an average accuracy and F1 score of 98% for 6D input size. The potential implications of this research are particularly relevant in the realm of smart healthcare, with a focus on the elderly and differently-abled population.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Falling poses a significant challenge to the health and well-being of the elderly and people with various disabilities. Precise and prompt fall detection plays a crucial role in preventing falls and mitigating the impact of injuries. In this research, we propose a deep classifier for pre-impact fall detection which can detect a fall in the pre-impact phase with an inference time of 46–52 milliseconds. The proposed classifier is an ensemble of Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRU) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available preimpact fall dataset. The dataset covers 36 diverse activities, including 15 types of fall-related activities and 21 types of activities of daily living (ADLs). Furthermore, we evaluated the proposed model using three different inputs of varying dimensions: 6D input (comprising 3D accelerations and 3D angular velocities), 3D input (3D accelerations), and 1D input (magnitude of 3D accelerations). The reduction in the input space from 6D to 1D is aimed at minimizing the computation cost. We have attained commendable results outperforming the state-of-the-art approaches by achieving an average accuracy and F1 score of 98% for 6D input size. The potential implications of this research are particularly relevant in the realm of smart healthcare, with a focus on the elderly and differently-abled population. Krüger, Björn; Weber, Christian; Müllers, Johannes; Greß, Hannah; Beyer, Franziska; Knaub, Jessica; Pukropski, Jan; Hütwohl, Daniela; Hahn, Kai; Grond, Martin; Jonas, Stephan; Surges, Rainer
Teleconsultation to Improve Epilepsy Diagnosis and Therapy Book Chapter
In: Herrmann, Wolfram J.; Leser, Ulf; Möller, Sebastian; Voigt-Antons, Jan-Niklas; Gellert, Paul (Ed.): pp. 18-23, Future-Proofing Healthcare for Older Adults Through Digitalization, 2024.
@inbook{krueger2024a,
title = {Teleconsultation to Improve Epilepsy Diagnosis and Therapy},
author = {Björn Krüger and Christian Weber and Johannes Müllers and Hannah Greß and Franziska Beyer and Jessica Knaub and Jan Pukropski and Daniela Hütwohl and Kai Hahn and Martin Grond and Stephan Jonas and Rainer Surges},
editor = {Wolfram J. Herrmann and Ulf Leser and Sebastian Möller and Jan-Niklas Voigt-Antons and Paul Gellert},
doi = {10.14279/depositonce-20417},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
pages = {18-23},
edition = {Future-Proofing Healthcare for Older Adults Through Digitalization},
abstract = {Teleconsultation in epileptology significantly enhances patient diagnosis and treatment, often eliminating the necessity for physical referral to a specialized clinic. In this paper, we detail the typical teleconsultation process, exploring its technical requirements and legal boundaries. Notably, we focus on the groundwork for establishing a teleconsultation specifically between the University Hospital Bonn and the Klinikum Siegen. Additionally, we provide an overview of currently implemented teleconsultations in epileptology in Germany, concluding with research questions stemming from these advancements. },
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Teleconsultation in epileptology significantly enhances patient diagnosis and treatment, often eliminating the necessity for physical referral to a specialized clinic. In this paper, we detail the typical teleconsultation process, exploring its technical requirements and legal boundaries. Notably, we focus on the groundwork for establishing a teleconsultation specifically between the University Hospital Bonn and the Klinikum Siegen. Additionally, we provide an overview of currently implemented teleconsultations in epileptology in Germany, concluding with research questions stemming from these advancements. Riedlinger, Dorothee; Krüger, Björn; Winkler, Hanna; Deutschbein, Johannes; Fischer-Rosinský, Antje; Slagman, Anna; Möckel, Martin
In: Herrmann, Wolfram J.; Leser, Ulf; Möller, Sebastian; Voigt-Antons, Jan-Niklas; Gellert, Paul (Ed.): pp. 108-113, Future-Proofing Healthcare for Older Adults Through Digitalization, 2024.
@inbook{riedlinger2024,
title = {Development of an early warning system to identify patients at risk of falling – Combining the analysis of medication prescription data and movement profiles},
author = {Dorothee Riedlinger and Björn Krüger and Hanna Winkler and Johannes Deutschbein and Antje Fischer-Rosinský and Anna Slagman and Martin Möckel},
editor = {Wolfram J. Herrmann and Ulf Leser and Sebastian Möller and Jan-Niklas Voigt-Antons and Paul Gellert},
doi = {10.14279/depositonce-20431},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
pages = {108-113},
edition = {Future-Proofing Healthcare for Older Adults Through Digitalization},
abstract = {Fall related injuries are a common cause for a reduction of autonomy and quality of life in older patients. The early detection of patients at risk of falling or the prediction of falls may help to prevent falls and thereby improve the health of people of advanced age. Prior analyses of routine medication data pointed to an increase of pain medication prescription prior to an ED },
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Fall related injuries are a common cause for a reduction of autonomy and quality of life in older patients. The early detection of patients at risk of falling or the prediction of falls may help to prevent falls and thereby improve the health of people of advanced age. Prior analyses of routine medication data pointed to an increase of pain medication prescription prior to an ED Greß, Hannah; Krüger, Björn
Security of Bluetooth-capable devices in the healthcare sector Proceedings Article
In: Ohm, Marc (Ed.): Proceedings of the 14th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2024), pp. 13-14, GI SIG SIDAR, Bonn, Germany, 2024, ISSN: 2190-846X.
@inproceedings{Greß2024,
title = {Security of Bluetooth-capable devices in the healthcare sector},
author = {Hannah Greß and Björn Krüger},
editor = {Marc Ohm},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
issn = {2190-846X},
year = {2024},
date = {2024-06-30},
urldate = {2024-06-30},
booktitle = { Proceedings of the 14th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2024)},
journal = {Proceedings of the 14th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2024) },
pages = {13-14},
publisher = {GI SIG SIDAR},
address = {Bonn, Germany},
abstract = {The steady growth of Internet of Medical Things (IoMT) devices collecting, storing and transmitting sensitive data, mostly over Bluetooth Low Energy (BLE), increases also the demand to test them regarding their security. Therefore, this work aims to give an overview of already existing Bluetooth pentesting tools and frameworks, BLE specific attacks and their countermeasures as well as to develop a framework which implements all of these to fasten the security testing process of IoMT wearables.},
keywords = {},
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The steady growth of Internet of Medical Things (IoMT) devices collecting, storing and transmitting sensitive data, mostly over Bluetooth Low Energy (BLE), increases also the demand to test them regarding their security. Therefore, this work aims to give an overview of already existing Bluetooth pentesting tools and frameworks, BLE specific attacks and their countermeasures as well as to develop a framework which implements all of these to fasten the security testing process of IoMT wearables. Hamza, Kiran; Riaz, Qaiser; Imran, Hamza Ali; Hussain, Mehdi; Krüger, Björn
Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things Journal Article
In: Sensors, vol. 24, no. 19, 2024, ISSN: 1424-8220.
@article{s24196167,
title = {Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things},
author = {Kiran Hamza and Qaiser Riaz and Hamza Ali Imran and Mehdi Hussain and Björn Krüger},
url = {https://www.mdpi.com/1424-8220/24/19/6167},
doi = {10.3390/s24196167},
issn = {1424-8220},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Sensors},
volume = {24},
number = {19},
abstract = {The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person’s psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.},
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The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person’s psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.
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}
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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 = {},
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Greß, Hannah; Dahl, Alanis; Tran, Mindy; Popovski, Marija Turkovic; Krüger, Björn
Theory vs. Practice: How Secure are Bluetooth Low Energy-Capable Health Devices Compared to Legal Requirements? Proceedings Article
In: Proceedings of the 16th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2026), pp. 46-48, German Informatics Society (GI), 2026, ISBN: ISSN 2190-846X.
@inproceedings{gress2026b,
title = {Theory vs. Practice: How Secure are Bluetooth Low Energy-Capable Health Devices Compared to Legal Requirements?},
author = {Hannah Greß and Alanis Dahl and Mindy Tran and Marija Turkovic Popovski and Björn Krüger
},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
isbn = {ISSN 2190-846X},
year = {2026},
date = {2026-05-11},
urldate = {2026-05-11},
booktitle = {Proceedings of the 16th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2026)},
pages = {46-48},
publisher = {German Informatics Society (GI)},
abstract = {In a world becoming increasingly ’smart’, be it in industry, at home, or in medicine, secure data storage and transmission become even more important. But how secure is this in practice, and do vendors comply with current law to adequately protect the users’ or companies’ data? We chose empatica’s EpiMonitor as a ’smart’ and medically certified device to exemplify what such a technical assessment and legal analysis could look and which vulnerabilities may occur. This assessment and analysis are transferable to other wearable devices, possibly with slight modifications.},
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In a world becoming increasingly ’smart’, be it in industry, at home, or in medicine, secure data storage and transmission become even more important. But how secure is this in practice, and do vendors comply with current law to adequately protect the users’ or companies’ data? We chose empatica’s EpiMonitor as a ’smart’ and medically certified device to exemplify what such a technical assessment and legal analysis could look and which vulnerabilities may occur. This assessment and analysis are transferable to other wearable devices, possibly with slight modifications.
Greß, Hannah; Schreiter, Jonas; Rademacher, Michael; Krüger, Björn
BLE Security Testing: Survey of Attacks and Evaluation of Tools and Frameworks Journal Article
In: IEEE Open Journal of the Communications Society, 2026.
@article{gress2026c,
title = {BLE Security Testing: Survey of Attacks and Evaluation of Tools and Frameworks},
author = {Hannah Greß and Jonas Schreiter and Michael Rademacher and Björn Krüger },
doi = {10.1109/OJCOMS.2026.3691569},
year = {2026},
date = {2026-05-08},
urldate = {2026-05-08},
journal = {IEEE Open Journal of the Communications Society},
abstract = {Nowadays, more and more areas of our lives are becoming ‘smart,’ including homes, industrial
sites, and medical devices. Since sensitive data is transmitted over various protocols, manufacturers must
ensure that the data is adequately secured. To do so, penetration testing on these devices is an option,
ideally before market launch. Depending on the protocol, various tools and frameworks exist. We chose
the Bluetooth Low Energy (BLE) protocol for analysis due to its widespread use and started our work by
classifying possible threats to BLE into four categories based on the attacks executable by the identified
tools and frameworks. Threats targeting BLE Mesh and BLE privacy and localization were identified
through a literature review and added to the corresponding categories of our taxonomy. Subsequently, we
conducted an in-depth evaluation of these tools and frameworks targeting BLE (excluding BLE Mesh,
and BLE privacy and localization) on four medical devices to empirically determine which tools and
frameworks remain usable for pentesting BLE devices. Furthermore, we analyzed which threats in our
taxonomy remain feasible. Our results show that only eight out of 21 (38%) attacks can still be carried
out. The tools and frameworks capable of conducting these attacks are mostly still maintained, emphasizing
the importance of keeping such tools and frameworks up to date for future use. Nevertheless, current BLE
pentesting requires a patchwork of multiple, often unmaintained frameworks to realize all known attacks
— this demonstrates an urgent need for a single, extensible, actively maintained framework that allows
attacks to be integrated modularly.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nowadays, more and more areas of our lives are becoming ‘smart,’ including homes, industrial
sites, and medical devices. Since sensitive data is transmitted over various protocols, manufacturers must
ensure that the data is adequately secured. To do so, penetration testing on these devices is an option,
ideally before market launch. Depending on the protocol, various tools and frameworks exist. We chose
the Bluetooth Low Energy (BLE) protocol for analysis due to its widespread use and started our work by
classifying possible threats to BLE into four categories based on the attacks executable by the identified
tools and frameworks. Threats targeting BLE Mesh and BLE privacy and localization were identified
through a literature review and added to the corresponding categories of our taxonomy. Subsequently, we
conducted an in-depth evaluation of these tools and frameworks targeting BLE (excluding BLE Mesh,
and BLE privacy and localization) on four medical devices to empirically determine which tools and
frameworks remain usable for pentesting BLE devices. Furthermore, we analyzed which threats in our
taxonomy remain feasible. Our results show that only eight out of 21 (38%) attacks can still be carried
out. The tools and frameworks capable of conducting these attacks are mostly still maintained, emphasizing
the importance of keeping such tools and frameworks up to date for future use. Nevertheless, current BLE
pentesting requires a patchwork of multiple, often unmaintained frameworks to realize all known attacks
— this demonstrates an urgent need for a single, extensible, actively maintained framework that allows
attacks to be integrated modularly.
sites, and medical devices. Since sensitive data is transmitted over various protocols, manufacturers must
ensure that the data is adequately secured. To do so, penetration testing on these devices is an option,
ideally before market launch. Depending on the protocol, various tools and frameworks exist. We chose
the Bluetooth Low Energy (BLE) protocol for analysis due to its widespread use and started our work by
classifying possible threats to BLE into four categories based on the attacks executable by the identified
tools and frameworks. Threats targeting BLE Mesh and BLE privacy and localization were identified
through a literature review and added to the corresponding categories of our taxonomy. Subsequently, we
conducted an in-depth evaluation of these tools and frameworks targeting BLE (excluding BLE Mesh,
and BLE privacy and localization) on four medical devices to empirically determine which tools and
frameworks remain usable for pentesting BLE devices. Furthermore, we analyzed which threats in our
taxonomy remain feasible. Our results show that only eight out of 21 (38%) attacks can still be carried
out. The tools and frameworks capable of conducting these attacks are mostly still maintained, emphasizing
the importance of keeping such tools and frameworks up to date for future use. Nevertheless, current BLE
pentesting requires a patchwork of multiple, often unmaintained frameworks to realize all known attacks
— this demonstrates an urgent need for a single, extensible, actively maintained framework that allows
attacks to be integrated modularly.
Jansen, Anna; Waldow, Kristoffer; Pötter, Sebastian; Civelek, Turhan; Steininger, Melissa; Perret, Jerome; Wellmann, Markus; Stein, Steffen-Sascha; Lähner, David; Welle, Kristian; Fuhrmann, Arnulph; Krüger, Björn
VIRTOSHA - A VR Training Simulation for Osteosynthesis Procedures with Force Feedback and Tissue Simulation Proceedings Article
In: 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) , pp. 921-930, IEEE Computer Society, 2026.
@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},
url = {https://doi.ieeecomputersociety.org/10.1109/VRW70859.2026.00171},
doi = {10.1109/VRW70859.2026.00171},
year = {2026},
date = {2026-03-31},
urldate = {2026-03-31},
booktitle = {2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) },
pages = {921-930},
publisher = {IEEE Computer Society},
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 = {published},
tppubtype = {inproceedings}
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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.
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.
Steininger, Melissa; Jansen, Anna; Müllers, Johannes; von Wrede, Randi; Krüger, Björn
Toward Interpretable Cognitive Screening in Epilepsy: Eye Tracking in a VR Trail Making Test Proceedings Article
In: IEEE VR 2026 Workshop: GEMINI, 2026.
@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},
url = {https://www.computer.org/csdl/proceedings-article/vrw/2026/052900a106/2gdqrMPo2s0},
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 = {published},
tppubtype = {inproceedings}
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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.
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.
Imran, Hamza Ali; Riaz, Qaiser; Hamza, Kiran; Muhammad, Shaida; Krüger, Björn
From Steps to Sentiments: Cross-Domain Transfer Learning for Activity-Based Emotion Detection in Wearable IoT Systems Journal Article
In: IEEE Internet of Things Journal, pp. 1-1, 2026.
@article{Imran2026a,
title = {From Steps to Sentiments: Cross-Domain Transfer Learning for Activity-Based Emotion Detection in Wearable IoT Systems},
author = {Hamza Ali Imran and Qaiser Riaz and Kiran Hamza and Shaida Muhammad and Björn Krüger},
doi = {10.1109/JIOT.2026.3666469},
year = {2026},
date = {2026-02-20},
urldate = {2026-02-20},
journal = {IEEE Internet of Things Journal},
pages = {1-1},
abstract = {Context-aware, gait-based sentiment analysis and emotion perception is an emerging research area within Internet of Things (IoT), aiming to make smart systems more intuitive and responsive. Recognizing emotions from wearable inertial sensor data is challenging due to subtle and compound emotional cues, variability across individuals and contexts, and limited, imbalanced datasets. To address these challenges, we propose Jazbat-Net, a lightweight neural network that leverages Transfer Learning (TL). The model is first trained on a large-scale, publicly available multi-activity dataset collected using wearable inertial sensors, and then retrained on a multi-class emotion dataset, effectively transferring knowledge from the pretraining phase. We evaluate Jazbat-Net with and without TL, across both smartwatch and smartphone based data, and for input dimensions ranging from 1D to 6D. The best results are achieved when pretrained on smartphone-based activity data and retrained on smartphone-based emotion data using a 1D input size. The proposed model attains an average classification accuracy of 95%, with a precision score of 95%, a recall score of 97%, and an F1-score of 96%. Moreover, Jazbat-Net achieves a low theoretical time complexity and requires only ≈ 6.96 M Multiply–Accumulate Operations (MACs), which is about 95% fewer computations than the previous State-of-the-Art (SOTA) model. Its space complexity is also low, with a model size of only ≈ 110 KB and peak activation memory of ≈ 0.35 MB. On-device evaluation on a Xiaomi 13T smartphone demonstrates that Jazbat-Net achieves a median inference latency of only ≈ 90.96 ms with a TFLite 32-bit floating point precision (FP32) model size of just ≈ 0.158 MB, making it ≈ 20× smaller and ≈ 20% faster than the previous SOTA model while maintaining comparable accuracy.
},
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Context-aware, gait-based sentiment analysis and emotion perception is an emerging research area within Internet of Things (IoT), aiming to make smart systems more intuitive and responsive. Recognizing emotions from wearable inertial sensor data is challenging due to subtle and compound emotional cues, variability across individuals and contexts, and limited, imbalanced datasets. To address these challenges, we propose Jazbat-Net, a lightweight neural network that leverages Transfer Learning (TL). The model is first trained on a large-scale, publicly available multi-activity dataset collected using wearable inertial sensors, and then retrained on a multi-class emotion dataset, effectively transferring knowledge from the pretraining phase. We evaluate Jazbat-Net with and without TL, across both smartwatch and smartphone based data, and for input dimensions ranging from 1D to 6D. The best results are achieved when pretrained on smartphone-based activity data and retrained on smartphone-based emotion data using a 1D input size. The proposed model attains an average classification accuracy of 95%, with a precision score of 95%, a recall score of 97%, and an F1-score of 96%. Moreover, Jazbat-Net achieves a low theoretical time complexity and requires only ≈ 6.96 M Multiply–Accumulate Operations (MACs), which is about 95% fewer computations than the previous State-of-the-Art (SOTA) model. Its space complexity is also low, with a model size of only ≈ 110 KB and peak activation memory of ≈ 0.35 MB. On-device evaluation on a Xiaomi 13T smartphone demonstrates that Jazbat-Net achieves a median inference latency of only ≈ 90.96 ms with a TFLite 32-bit floating point precision (FP32) model size of just ≈ 0.158 MB, making it ≈ 20× smaller and ≈ 20% faster than the previous SOTA model while maintaining comparable accuracy.
Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas
In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, vol. 63, pp. 00469580251413101, 2026.
@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},
url = {https://doi.org/10.1177/00469580251413101},
doi = {10.1177/00469580251413101},
year = {2026},
date = {2026-01-28},
urldate = {2025-12-01},
journal = {INQUIRY: The Journal of Health Care Organization, Provision, and Financing},
volume = {63},
pages = {00469580251413101},
abstract = {Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Post-traumatic stress disorder (PTSD) is often debilitating, with current treatments limited by low adherence, high costs, and accessibility issues. Innovative technologies such as virtual reality (VR), augmented reality (AR), and therapeutic video games provide immersive environments that may improve treatment outcomes. This systematic review and meta-analysis evaluated the efficacy of these approaches and explored their potential advantages over traditional methods. A comprehensive search of PubMed, PsycINFO, CINAHL, Web of Science, and Cochrane identified relevant studies. Two reviewers independently screened articles, extracted data, and assessed quality using the Mixed Methods Appraisal Tool (MMAT). A random-effects model was used to calculate pooled effect sizes (Hedges’ g), and heterogeneity was evaluated with the Q test and I2 statistic. Publication bias was examined with funnel plots, Egger’s, and Begg’s tests. Analyses were performed in Stata version 17.0. From 480 records, 21 studies were included in the review and 12 in the meta-analysis. VR-based treatments yielded a pooled effect size of –0.35 (95% CI [–0.57, –0.13]), indicating a small-to-moderate reduction in PTSD symptoms. The effect was statistically significant (z = –3.13, P < .01), with moderate heterogeneity (I2 = 46.28%, P = .03). Funnel plots and statistical tests suggested minimal publication bias. Meta-regression showed no moderating effect of gender. Subgroup analyses indicated significant benefits in male-only samples, participants aged 20 to 30 and over 40, and studies with follow-up periods ≤7 months. Larger effects were observed in studies with 15 to 30 participants. VR, AR, and video game interventions significantly reduce PTSD symptoms and may enhance accessibility and engagement compared to traditional treatments. These findings support the integration of immersive technologies into therapeutic practice to improve outcomes for individuals with PTSD. }
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}
}
Barzegar, Mohammad Mehdi; Daryakenari, Nazila Ahmadi; Khodatars, Marjane
Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model Journal Article
In: Journal of Research and Health, vol. 15, no. 6, 2025.
@article{Barzegar2025,
title = {Explainable Epileptic Seizure Detection from Electroencephalography Signals via CNN–Bi-LSTM Attention Hybrid Model},
author = {Mohammad Mehdi Barzegar and Nazila Ahmadi Daryakenari and Marjane Khodatars},
url = {http://jrh.gmu.ac.ir/article-1-2987-en.html},
doi = {10.32598/JRH.15.SP.2892.1},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Research and Health},
volume = {15},
number = {6},
abstract = {Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Epilepsy is a chronic neurological disorder marked by recurrent daily seizures that threaten patient safety. Electroencephalography (EEG) is a crucial neuroimaging tool for epilepsy diagnosis, but manual interpretation of EEG signals is challenging for clinicians. To assist specialists, automated systems, such as computer-aided diagnosis systems (CADS) based on deep learning (DL) are essential. Methods: The proposed CADS system was validated using the Turkish epilepsy dataset. In preprocessing, EEG signals were filtered, down-sampled, re-referenced using common average reference (CAR), and segmented into multiple temporal windows. A new feature extraction framework combining one-dimensional convolutional neural networks (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism was developed. All experiments were performed using 5-fold cross-validation. Post-hoc explainability was evaluated using explainable artificial intelligence (XAI) techniques, including t-distributed stochastic neighbor embedding (t-SNE) and shapley additive explanations (SHAP). Results: The proposed CADS achieved a seizure diagnosis accuracy of 99.49%, demonstrating high robustness across the validation folds, with minimal variance between folds (±0.12%). Feature space visualization confirmed clear class separation, and SHAP analysis provided clinically meaningful explanations for model decisions. Conclusion: The proposed DL architecture shows strong potential for reliable and interpretable automatic epileptic seizure detection from EEG. This CADS can significantly reduce the diagnostic burden on clinicians and support real-time decision-making in clinical environments.
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
},
url = {https://digital-health-bonn.de/wp-content/uploads/2026/05/2025_Steininger_Awe.pdf, Preprint version of the Paper},
doi = {10.1109/ISMAR67309.2025.00132},
year = {2025},
date = {2025-11-11},
urldate = {2025-11-11},
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.
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; 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.},
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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.},
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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.
2024
Otsubo, Hiromu; Lehnort, Marvin; Steininger, Melissa; Marquardt, Alexander; Dollack, Felix; Hirao, Yutaro; Perusquía-Hernández, Monica; Uchiyama, Hideaki; Kruijff, Ernst; Riecke, Bernhard; Kiyokawa, Kiyoshi
First-Person Perspective Induces Stronger Feelings of Awe and Presence Compared to Third-Person Perspective in Virtual Reality Proceedings Article
In: ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction, pp. 439 - 448, 2024.
@inproceedings{Otsubo2024,
title = {First-Person Perspective Induces Stronger Feelings of Awe and Presence Compared to Third-Person Perspective in Virtual Reality},
author = {Hiromu Otsubo and Marvin Lehnort and Melissa Steininger and Alexander Marquardt and Felix Dollack and Yutaro Hirao and Monica Perusquía-Hernández and Hideaki Uchiyama and Ernst Kruijff and Bernhard Riecke and Kiyoshi Kiyokawa},
doi = {https://doi.org/10.1145/3678957.3685753},
year = {2024},
date = {2024-11-04},
urldate = {2024-11-04},
booktitle = {ICMI '24: Proceedings of the 26th International Conference on Multimodal Interaction},
pages = {439 - 448},
abstract = {Awe is a complex emotion described as a perception of vastness and a need for accommodation to integrate new, overwhelming experiences. Virtual Reality (VR) has recently gained attention as a convenient means to facilitate experiences of awe. In VR, a first-person perspective might increase awe due to its immersive nature, while a third-person perspective might enhance the perception of vastness. However, the impact of VR perspectives on experiencing awe has not been thoroughly examined. We created two types of VR scenes: one with elements designed to induce high awe, such as a snowy mountain, and a low awe scene without such elements. We compared first-person and third-person perspectives in each scene. Forty-two participants explored the VR scenes, with their physiological responses captured by electrocardiogram (ECG) and face tracking (FT). Subsequently, participants self-reported their experience of awe (AWE-S) and presence (IPQ) within VR. The results revealed that the first-person perspective induced stronger feelings of awe and presence than the third-person perspective. The findings of this study provide useful guidelines for designing VR content that enhances emotional experiences.},
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Awe is a complex emotion described as a perception of vastness and a need for accommodation to integrate new, overwhelming experiences. Virtual Reality (VR) has recently gained attention as a convenient means to facilitate experiences of awe. In VR, a first-person perspective might increase awe due to its immersive nature, while a third-person perspective might enhance the perception of vastness. However, the impact of VR perspectives on experiencing awe has not been thoroughly examined. We created two types of VR scenes: one with elements designed to induce high awe, such as a snowy mountain, and a low awe scene without such elements. We compared first-person and third-person perspectives in each scene. Forty-two participants explored the VR scenes, with their physiological responses captured by electrocardiogram (ECG) and face tracking (FT). Subsequently, participants self-reported their experience of awe (AWE-S) and presence (IPQ) within VR. The results revealed that the first-person perspective induced stronger feelings of awe and presence than the third-person perspective. The findings of this study provide useful guidelines for designing VR content that enhances emotional experiences.
Marquardt, Alexander; Lehnort, Marvin; Otsubo, Hiromu; Perusquia-Hernandez, Monica; Steininger, Melissa; Dollack, Felix; Uchiyama, Hideaki; Kiyokawa, Kiyoshi; Kruijff, Ernst
Exploring Gesture Interaction in Underwater Virtual Reality Proceedings Article
In: Proceedings of the 2024 ACM Symposium on Spatial User Interaction, pp. 1-2, 2024.
@inproceedings{marquardtLehnort2024,
title = {Exploring Gesture Interaction in Underwater Virtual Reality},
author = {Alexander Marquardt and Marvin Lehnort and Hiromu Otsubo and Monica Perusquia-Hernandez and Melissa Steininger and Felix Dollack and Hideaki Uchiyama and Kiyoshi Kiyokawa and Ernst Kruijff},
doi = {https://doi.org/10.1145/3677386.3688890},
year = {2024},
date = {2024-10-07},
urldate = {2024-10-07},
booktitle = {Proceedings of the 2024 ACM Symposium on Spatial User Interaction},
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abstract = {An underwater virtual reality (UVR) system with gesture-based controls was developed to facilitate navigation and interaction while submerged. The system uses a waterproof head-mounted display and camera-based gesture recognition, originally trained for abovewater conditions, employing three gestures: grab for navigation, pinch for single interactions, and point for continuous interactions. In an experimental study, we tested gesture recognition both above and underwater, and evaluated participant interaction within an immersive underwater scene. Results showed that underwater conditions slightly affected gesture accuracy, but the system maintained high performance. Participants reported a strong sense of presence and found the gestures intuitive while highlighting the need for further refinement to address usability challenges.},
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An underwater virtual reality (UVR) system with gesture-based controls was developed to facilitate navigation and interaction while submerged. The system uses a waterproof head-mounted display and camera-based gesture recognition, originally trained for abovewater conditions, employing three gestures: grab for navigation, pinch for single interactions, and point for continuous interactions. In an experimental study, we tested gesture recognition both above and underwater, and evaluated participant interaction within an immersive underwater scene. Results showed that underwater conditions slightly affected gesture accuracy, but the system maintained high performance. Participants reported a strong sense of presence and found the gestures intuitive while highlighting the need for further refinement to address usability challenges.
Kiran, Samia; Riaz, Qaiser; Hussain, Mehdi; Zeeshan, Muhammad; Krüger, Björn
Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes Journal Article
In: IEEE Sensors Journal, vol. 24, no. 15, pp. 24086-24095, 2024, ISSN: 1558-1748.
@article{10552639,
title = {Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes},
author = {Samia Kiran and Qaiser Riaz and Mehdi Hussain and Muhammad Zeeshan and Björn Krüger},
doi = {10.1109/JSEN.2024.3407835},
issn = {1558-1748},
year = {2024},
date = {2024-08-01},
urldate = {2024-01-01},
journal = {IEEE Sensors Journal},
volume = {24},
number = {15},
pages = {24086-24095},
abstract = {Falling poses a significant challenge to the health and well-being of the elderly and people with various disabilities. Precise and prompt fall detection plays a crucial role in preventing falls and mitigating the impact of injuries. In this research, we propose a deep classifier for pre-impact fall detection which can detect a fall in the pre-impact phase with an inference time of 46–52 milliseconds. The proposed classifier is an ensemble of Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRU) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available preimpact fall dataset. The dataset covers 36 diverse activities, including 15 types of fall-related activities and 21 types of activities of daily living (ADLs). Furthermore, we evaluated the proposed model using three different inputs of varying dimensions: 6D input (comprising 3D accelerations and 3D angular velocities), 3D input (3D accelerations), and 1D input (magnitude of 3D accelerations). The reduction in the input space from 6D to 1D is aimed at minimizing the computation cost. We have attained commendable results outperforming the state-of-the-art approaches by achieving an average accuracy and F1 score of 98% for 6D input size. The potential implications of this research are particularly relevant in the realm of smart healthcare, with a focus on the elderly and differently-abled population.},
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Falling poses a significant challenge to the health and well-being of the elderly and people with various disabilities. Precise and prompt fall detection plays a crucial role in preventing falls and mitigating the impact of injuries. In this research, we propose a deep classifier for pre-impact fall detection which can detect a fall in the pre-impact phase with an inference time of 46–52 milliseconds. The proposed classifier is an ensemble of Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRU) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available preimpact fall dataset. The dataset covers 36 diverse activities, including 15 types of fall-related activities and 21 types of activities of daily living (ADLs). Furthermore, we evaluated the proposed model using three different inputs of varying dimensions: 6D input (comprising 3D accelerations and 3D angular velocities), 3D input (3D accelerations), and 1D input (magnitude of 3D accelerations). The reduction in the input space from 6D to 1D is aimed at minimizing the computation cost. We have attained commendable results outperforming the state-of-the-art approaches by achieving an average accuracy and F1 score of 98% for 6D input size. The potential implications of this research are particularly relevant in the realm of smart healthcare, with a focus on the elderly and differently-abled population.
Krüger, Björn; Weber, Christian; Müllers, Johannes; Greß, Hannah; Beyer, Franziska; Knaub, Jessica; Pukropski, Jan; Hütwohl, Daniela; Hahn, Kai; Grond, Martin; Jonas, Stephan; Surges, Rainer
Teleconsultation to Improve Epilepsy Diagnosis and Therapy Book Chapter
In: Herrmann, Wolfram J.; Leser, Ulf; Möller, Sebastian; Voigt-Antons, Jan-Niklas; Gellert, Paul (Ed.): pp. 18-23, Future-Proofing Healthcare for Older Adults Through Digitalization, 2024.
@inbook{krueger2024a,
title = {Teleconsultation to Improve Epilepsy Diagnosis and Therapy},
author = {Björn Krüger and Christian Weber and Johannes Müllers and Hannah Greß and Franziska Beyer and Jessica Knaub and Jan Pukropski and Daniela Hütwohl and Kai Hahn and Martin Grond and Stephan Jonas and Rainer Surges},
editor = {Wolfram J. Herrmann and Ulf Leser and Sebastian Möller and Jan-Niklas Voigt-Antons and Paul Gellert},
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year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
pages = {18-23},
edition = {Future-Proofing Healthcare for Older Adults Through Digitalization},
abstract = {Teleconsultation in epileptology significantly enhances patient diagnosis and treatment, often eliminating the necessity for physical referral to a specialized clinic. In this paper, we detail the typical teleconsultation process, exploring its technical requirements and legal boundaries. Notably, we focus on the groundwork for establishing a teleconsultation specifically between the University Hospital Bonn and the Klinikum Siegen. Additionally, we provide an overview of currently implemented teleconsultations in epileptology in Germany, concluding with research questions stemming from these advancements. },
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Teleconsultation in epileptology significantly enhances patient diagnosis and treatment, often eliminating the necessity for physical referral to a specialized clinic. In this paper, we detail the typical teleconsultation process, exploring its technical requirements and legal boundaries. Notably, we focus on the groundwork for establishing a teleconsultation specifically between the University Hospital Bonn and the Klinikum Siegen. Additionally, we provide an overview of currently implemented teleconsultations in epileptology in Germany, concluding with research questions stemming from these advancements.
Riedlinger, Dorothee; Krüger, Björn; Winkler, Hanna; Deutschbein, Johannes; Fischer-Rosinský, Antje; Slagman, Anna; Möckel, Martin
In: Herrmann, Wolfram J.; Leser, Ulf; Möller, Sebastian; Voigt-Antons, Jan-Niklas; Gellert, Paul (Ed.): pp. 108-113, Future-Proofing Healthcare for Older Adults Through Digitalization, 2024.
@inbook{riedlinger2024,
title = {Development of an early warning system to identify patients at risk of falling – Combining the analysis of medication prescription data and movement profiles},
author = {Dorothee Riedlinger and Björn Krüger and Hanna Winkler and Johannes Deutschbein and Antje Fischer-Rosinský and Anna Slagman and Martin Möckel},
editor = {Wolfram J. Herrmann and Ulf Leser and Sebastian Möller and Jan-Niklas Voigt-Antons and Paul Gellert},
doi = {10.14279/depositonce-20431},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
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edition = {Future-Proofing Healthcare for Older Adults Through Digitalization},
abstract = {Fall related injuries are a common cause for a reduction of autonomy and quality of life in older patients. The early detection of patients at risk of falling or the prediction of falls may help to prevent falls and thereby improve the health of people of advanced age. Prior analyses of routine medication data pointed to an increase of pain medication prescription prior to an ED },
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Fall related injuries are a common cause for a reduction of autonomy and quality of life in older patients. The early detection of patients at risk of falling or the prediction of falls may help to prevent falls and thereby improve the health of people of advanced age. Prior analyses of routine medication data pointed to an increase of pain medication prescription prior to an ED
Greß, Hannah; Krüger, Björn
Security of Bluetooth-capable devices in the healthcare sector Proceedings Article
In: Ohm, Marc (Ed.): Proceedings of the 14th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2024), pp. 13-14, GI SIG SIDAR, Bonn, Germany, 2024, ISSN: 2190-846X.
@inproceedings{Greß2024,
title = {Security of Bluetooth-capable devices in the healthcare sector},
author = {Hannah Greß and Björn Krüger},
editor = {Marc Ohm},
url = {https://fg-sidar.gi.de/publikationen/sidar-reports},
issn = {2190-846X},
year = {2024},
date = {2024-06-30},
urldate = {2024-06-30},
booktitle = { Proceedings of the 14th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2024)},
journal = {Proceedings of the 14th graduate workshop of the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI) (SPRING 2024) },
pages = {13-14},
publisher = {GI SIG SIDAR},
address = {Bonn, Germany},
abstract = {The steady growth of Internet of Medical Things (IoMT) devices collecting, storing and transmitting sensitive data, mostly over Bluetooth Low Energy (BLE), increases also the demand to test them regarding their security. Therefore, this work aims to give an overview of already existing Bluetooth pentesting tools and frameworks, BLE specific attacks and their countermeasures as well as to develop a framework which implements all of these to fasten the security testing process of IoMT wearables.},
keywords = {},
pubstate = {published},
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The steady growth of Internet of Medical Things (IoMT) devices collecting, storing and transmitting sensitive data, mostly over Bluetooth Low Energy (BLE), increases also the demand to test them regarding their security. Therefore, this work aims to give an overview of already existing Bluetooth pentesting tools and frameworks, BLE specific attacks and their countermeasures as well as to develop a framework which implements all of these to fasten the security testing process of IoMT wearables.
Hamza, Kiran; Riaz, Qaiser; Imran, Hamza Ali; Hussain, Mehdi; Krüger, Björn
Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things Journal Article
In: Sensors, vol. 24, no. 19, 2024, ISSN: 1424-8220.
@article{s24196167,
title = {Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things},
author = {Kiran Hamza and Qaiser Riaz and Hamza Ali Imran and Mehdi Hussain and Björn Krüger},
url = {https://www.mdpi.com/1424-8220/24/19/6167},
doi = {10.3390/s24196167},
issn = {1424-8220},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Sensors},
volume = {24},
number = {19},
abstract = {The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person’s psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.},
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The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person’s psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.