Publications – Digital Health Bonn

126 entries « ‹ 1 of 6 › »

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.

Gfesser, Torsten; Witte, Thomas; Krüger, Björn

On the Efficacy and Usability of Adaptive Instructional Systems Proceedings Article Forthcoming

In: HCI International 2026, Springer, Forthcoming.

Simsek, Koray; Müllers, Johannes; Surges, Rainer; Krüger, Björn

Kontaktloses kamerabasiertes Messen von Vitalparametern Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn

Kontextualisierte Eye-Tracking-Metriken zur Charakterisierung von Suchstrategien bei Personen mit Epilepsie und Kontrollen Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Greß, Hannah; Daryakenari, Nazila Ahmadi; Bungartz, Christian; Viola, Felix; Markwald, Marco; Brüll, Gabriela; Kumar, Uttam; Ohm, Marc; Surges, Rainer; Meier, Michael; Demidova, Elena; Krüger, Björn

Anforderungen an sichere KI-Modelle zur Anfallsdetektion mit Wearables in der Epileptologie Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Vetter, Jonas; Müllers, Johannes; Spurio, Federico; Surges, Rainer; Gall, Juergen; Krüger, Björn

Kontaktlose 3D-Human-Pose-Estimation im Video-EEG-Monitoring von Epilepsiepatienten Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Pukropski, Jan; Keßler, Lisa; von Wrede, Randi; Surges, Rainer; Krüger, Björn

Elektrokardiographische Veränderungen unter Cenobamat – eine retrospektive Prä-Post-Analyse aus verlängerten EKG-Ableitungen bei Patient*innen mit Epilepsie Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn

Zusammenhänge zwischen Anfallssuppressiva und kontextualisierten Eye-Tracking-Metriken bei Menschen mit Epilepsie Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Mustafa, Sarah Al-Haj; Jansen, Anna; Steininger, Melissa; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; Krüger, Björn; von Wrede, Randi

Wer suchet, der findet: Eye Tracking beim Trail Making Test bei Epilepsie – Zusammenhänge mit depressiver Symptomatik Conference Forthcoming

64. Jahrestagung der Deutschen Gesellschaft für Epileptologie, Forthcoming.

Kretschmer-Trendowicz, Anett; Moser, Florian; Gürster, Lena; Pippirs, Corinna; Maas, Pia; Zeiler, Anne; Steininger, Melissa; Walk, Simon; von Bock, Christian; Krüger, Björn; Spittler, Thomas

Virtual Interaction to Promote Mental Health in Children with Social Anxiety Disorders (VISAKI) Conference Forthcoming

European Congress of Psychiatry 2026, Forthcoming.

Steininger, Melissa; Jansen, Anna; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn

Linking Higher-level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients Conference Forthcoming

4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.

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 Forthcoming

In: IEEE VR 2026 Workshop: XR-MED, Forthcoming.

Abstract | BibTeX

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 Forthcoming

In: IEEE VR 2026 Workshop: GEMINI, Forthcoming.

Abstract | BibTeX

Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Bouzan, Nataly; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn

Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls Conference Forthcoming

4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Forthcoming.

Müllers, Johannes; Siddiquie, Usama; Lemken, Johannes; Staehle, Ricarda; Schulte-Rüther, Martin; Krüger, Björn

MARVEL: A Human-in-the-Loop Web Platform for Multimodal Annotation and Classification of Social Behavior Conference Forthcoming

17th Autism Spectrum Scientific Conference, Forthcoming.

Staehle, Ricarda; Siddiquie, Usama; Müllers, Johannes; Krüger, Björn; Poustka, Luise; Schulte-Rüther, Martin

Clinical Annotation of Socio-Emotional Signals in Autism: Facilitating Diagnostic Review, Consensus Building, and Machine Learning Applications Conference Forthcoming

17th Autism Spectrum Scientific Conference, Forthcoming.

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.

Abstract | Links | BibTeX

@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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Goharinejad, Saeideh; Goharinezhad, Salime; Moulaei, Khadijeh; Krüger, Björn; Spittler, Thomas

Assessing the Impact of Virtual Reality, Augmented Reality, and Video Games on Improving Post-Traumatic Stress Disorder Symptoms: A Systematic Review and Meta-Analysis Journal Article

In: INQUIRY: The Journal of Health Care Organization, Provision, and Financing, vol. 63, pp. 00469580251413101, 2026.

Abstract | Links | BibTeX

@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 = {},

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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.

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.

Abstract | Links | BibTeX

@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 = {},

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tppubtype = {article}

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Moontaha, Sidratul; Cavalier, Constanze; Esser, Birgitta; Jordan, Arthur; Goebel, Ines; Anders, Christoph; Mimi, Afsana; Krüger, Björn; Surges, Rainer; Arnrich, Bert

EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients Journal Article

In: Scientific Data, vol. 12, iss. 1, no. 1867, 2025, ISBN: 2052-4463.

Abstract | Links | BibTeX

@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.},

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tppubtype = {article}

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Daryakenari, Nazila Ahmadi; Setarehdan, Seyed Kamaledin

EEG-based Schizophrenia Detection Using Spectral, Entropy, and Graph Connectivity Features with Machine Learning Conference

Proceedings of the 32nd National and 10th International Iranian Conference on Biomedical Engineering (ICBME 2025), 2025.

Abstract | Links | BibTeX

@conference{nokey,

title = {EEG-based Schizophrenia Detection Using Spectral, Entropy, and Graph Connectivity Features with Machine Learning},

author = {Nazila Ahmadi Daryakenari and Seyed Kamaledin Setarehdan},

url = {https://www.researchgate.net/publication/398572103_EEG-based_Schizophrenia_Detection_Using_Spectral_Entropy_and_Graph_Connectivity_Features_with_Machine_Learning},

year  = {2025},

date = {2025-11-20},

urldate = {2025-11-20},

publisher = {Proceedings of the 32nd National and 10th International Iranian Conference on Biomedical Engineering (ICBME 2025)},

abstract = {Schizophrenia is a serious mental disorder that changes the way people think, perceive, and manage daily life. Getting the diagnosis right is critical for proper treatment, but in practice it is often difficult. Current evaluations depend mostly on a clinician's judgment, and the overlap of symptoms with bipolar disorder or major depression makes the task even harder. EEG offers a safe and noninvasive way to study brain activity, yet no single EEG feature has been reliable enough to stand on its own. This makes it important to look at integrative approaches that bring together different aspects of brain dynamics. In this study, we analyzed EEG features to distinguish patients with schizophrenia from healthy controls. Spectral power was measured across δ, θ, α, β, and γ bands. Temporal irregularity was quantified with Multiscale Permutation Entropy (MPE), which to our knowledge represents the first application of MPE to EEG in schizophrenia. Functional connectivity was estimated with the weighted Phase Lag Index in θ, α, and β bands, followed by extraction of graph measures including global efficiency, clustering coefficient, characteristic path length, and mean strength. These features were used to train Random Forest, Multi-Layer Perceptron, and Support Vector Machine classifiers. Among the models, Random Forest achieved the most reliable performance, reaching 99.7% accuracy under stratified 5-fold validation and 99.6% under leave-one-subject-out validation. Feature analysis showed that connectivity in θ and α bands contributed most strongly to classification. Topographic maps of θ, α, and β activity also revealed regional group differences. Overall, the results suggest that combining spectral, entropy, and connectivity measures offers a promising framework for EEG-based detection of schizophrenia. Nevertheless, these findings are preliminary given the limited sample size (N=28), and replication in larger and more diverse cohorts is required before clinical translation.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schizophrenia is a serious mental disorder that changes the way people think, perceive, and manage daily life. Getting the diagnosis right is critical for proper treatment, but in practice it is often difficult. Current evaluations depend mostly on a clinician's judgment, and the overlap of symptoms with bipolar disorder or major depression makes the task even harder. EEG offers a safe and noninvasive way to study brain activity, yet no single EEG feature has been reliable enough to stand on its own. This makes it important to look at integrative approaches that bring together different aspects of brain dynamics. In this study, we analyzed EEG features to distinguish patients with schizophrenia from healthy controls. Spectral power was measured across δ, θ, α, β, and γ bands. Temporal irregularity was quantified with Multiscale Permutation Entropy (MPE), which to our knowledge represents the first application of MPE to EEG in schizophrenia. Functional connectivity was estimated with the weighted Phase Lag Index in θ, α, and β bands, followed by extraction of graph measures including global efficiency, clustering coefficient, characteristic path length, and mean strength. These features were used to train Random Forest, Multi-Layer Perceptron, and Support Vector Machine classifiers. Among the models, Random Forest achieved the most reliable performance, reaching 99.7% accuracy under stratified 5-fold validation and 99.6% under leave-one-subject-out validation. Feature analysis showed that connectivity in θ and α bands contributed most strongly to classification. Topographic maps of θ, α, and β activity also revealed regional group differences. Overall, the results suggest that combining spectral, entropy, and connectivity measures offers a promising framework for EEG-based detection of schizophrenia. Nevertheless, these findings are preliminary given the limited sample size (N=28), and replication in larger and more diverse cohorts is required before clinical translation.

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.

Abstract | Links | BibTeX

@inproceedings{steininger2025c,

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

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

},

doi = {10.1109/ISMAR67309.2025.00132},

year  = {2025},

date = {2025-11-11},

urldate = {2025-10-01},

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

pages = {1267-1277},

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

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

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

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

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

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

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tppubtype = {inproceedings}

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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.

Abstract | Links | BibTeX

Alavi, Khashayar; Jansen, Anna; Steininger, Melissa; Mustafa, Sarah Al-Haj; Müllers, Johannes; Surges, Rainer; Helmstaedter, Christoph; von Wrede, Randi; Krüger, Björn

Graph Neural Networks for Analyzing Eye Fixation Patterns in Epilepsy Conference

International Congress on Mobile Health and Digital Technology in Epilepsy, 2025.

126 entries « ‹ 1 of 6 › »