Publications – Digital Health Bonn

Publications – Digital Health Bonn https://digital-health-bonn.de Working Group "Personalized Digital Health and Telemedicine" Mon, 09 Mar 2026 15:51:15 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://digital-health-bonn.de/wp-content/uploads/2024/02/cropped-cropped-uni-ukb-logo-32x32.png Publications – Digital Health Bonn https://digital-health-bonn.de 32 32 Seven Contributions from Our Group at the DGfE 2026 Annual Meeting https://digital-health-bonn.de/seven-contributions-from-our-group-at-the-dgfe-2026-annual-meeting/ Mon, 09 Mar 2026 15:34:35 +0000 https://digital-health-bonn.de/?p=770 Researchers from the Personalized Digital Health and Telemedicine Group at the University Hospital Bonn (UKB) will present seven scientific contributions at the 64th Annual Meeting of the German Society for Epileptology (DGfE) in Würzburg.

The accepted contributions highlight our group’s interdisciplinary research at the intersection of artificial intelligence, digital health, computer vision, wearable sensing, and clinical epileptology. Our work focuses on the development of digital biomarkers and AI-based analysis methods to improve the monitoring, diagnosis, and understanding of epilepsy.

Research Topics

The presented studies cover several emerging directions in AI-driven epilepsy research, including:

Contactless measurement of vital parameters using camera-based sensing technologies
3D human pose estimation for video-EEG monitoring to analyze seizure-related body movements
Eye-tracking metrics for cognitive assessment in epilepsy, including contextualized analysis of visual search strategies
Effects of anti-seizure medication on eye-tracking parameters in patients with epilepsy
Eye tracking during the Trail Making Test and its association with depressive symptoms
Secure and trustworthy AI models for seizure detection using wearable devices
Electrocardiographic changes under cenobamate therapy based on extended ECG recordings

These contributions reflect ongoing collaborations between computer scientists, physicists, neurologists, and clinical researchers at the University Hospital Bonn and partner institutions.

A large part of the work has been carried out by PhD students and early-career researchers, demonstrating the strong integration of young scientists in our research activities.

We look forward to presenting our work in Würzburg and discussing these results with colleagues from the international epilepsy research and digital medicine community.

2026

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.

]]> New Publication: Activity-Based Emotion Detection Using Wearable Sensors and Transfer Learning https://digital-health-bonn.de/new-publication-activity-based-emotion-detection-using-wearable-sensors-and-transfer-learning/ Wed, 25 Feb 2026 15:40:24 +0000 https://digital-health-bonn.de/?p=773 Understanding human emotions from everyday behavior is an important goal in digital health, affective computing, and wearable sensing. In our latest publication in the IEEE Internet of Things Journal, we investigate how movement data from wearable sensors can be used to infer emotional states.

The paper “From Steps to Sentiments: Cross-Domain Transfer Learning for Activity-Based Emotion Detection in Wearable IoT Systems” explores how machine learning models trained for activity recognition can be adapted to detect emotional states. Using a cross-domain transfer learning approach, the study demonstrates how knowledge learned from physical activity data can be transferred to emotion recognition tasks.

Wearable devices continuously capture movement patterns, step dynamics, and physical activity signals, which provide valuable information about human behavior in everyday environments. By leveraging these signals, the proposed approach opens new opportunities for non-invasive emotion monitoring using wearable IoT systems.

Such technologies have potential applications in mental health monitoring, digital phenotyping, and personalized healthcare, where unobtrusive sensing and intelligent data analysis can support early detection of behavioral and emotional changes.

This publication also continues a long-standing research collaboration between Qaiser Riaz and Björn Krüger, focusing on machine learning methods for human movement analysis and wearable sensor systems.

The full publication can be accessed via IEEE Xplore:
https://ieeexplore.ieee.org/document/11404152

2026

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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Close

]]> Abstracts Accepted for International AI in Epilepsy Conference https://digital-health-bonn.de/abstracts-accepted-for-international-ai-in-epilepsy-conference/ Mon, 22 Dec 2025 16:14:09 +0000 https://digital-health-bonn.de/?p=711 We are pleased to share some good news at the end of the year: two abstracts from our group have been accepted for the 4th International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, which will take place in Puerto Rico from March 16–19, 2026.

The accepted contributions are:

Linking Higher-Level Eye Tracking Metrics to High-Impact Antiseizure Medication in Epilepsy Patients
Higher-Level Eye Tracking Metrics Reveal Search Behaviour Differences in Persons with Epilepsy vs. Healthy Controls

Anna Jansen and Melissa Steininger will present this work at the conference.

We look forward to continuing our research on digital health and AI in epilepsy in the coming year and wish everyone a restful holiday season.

]]> 🎉 Exciting News to Close the Year! 🎉 https://digital-health-bonn.de/%f0%9f%8e%89-exciting-news-to-close-the-year-%f0%9f%8e%89/ Thu, 19 Dec 2024 13:22:02 +0000 https://digital-health-bonn.de/?p=526 As the year comes to an end, we’re thrilled to share some updates from our research group: several of our submissions have been accepted for conferences in 2025!

Accepted Contributions – Dreiländertagung Epilepsie 2025 in Salzburg:
Digital Transformation of Blood Sample Management
EpiEye – Effects of Seizure-Suppressive Medications on Eye Movements and Autonomic Changes in Epilepsy
Establishing an Epileptological Teleconsultation Between Siegen Hospital and University Hospital Bonn
ANNE – Eye-Tracking for Detecting Side Effects in Epilepsy
Semiological Characteristics of Temporal Lobe Epilepsy with GAD65 Antibodies

Accepted Contributions – International Conference on Artificial Intelligence in Epilepsy and Other Neurological Disorders 2025:
Eye-Tracking Reveals Search Behaviour in Epilepsy Patients
Prediction Models on Eye-Tracking Data in Epilepsy

Accepted Contribution – Wissenschaftlichen Tagung Autismus-Spektrum (WTAS)
Multi-Stream Analysis for Robust Head Gesture Classification in Natural Social Interaction: Leveraging High-Resolution Sensor Data for Optimized Visual Classification

We sincerely thank all contributors for their dedication and hard work. We look forward to engaging discussions, inspiring exchanges, and exciting collaborations at these upcoming conferences!

Wishing everyone a successful and inspiring year ahead!

]]> Pre-Impact Fall Detection: Paper Accepted! https://digital-health-bonn.de/pre-impact-fall-detection-paper-accepted/ Mon, 10 Jun 2024 15:46:08 +0000 https://digital-health-bonn.de/?p=332 We are thrilled to announce that our latest research paper, “Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes,” has been accepted for publication in the IEEE Sensors Journal. Our study focuses on developing a deep classifier to detect falls in the pre-impact phase using wearable sensors, achieving outstanding accuracy and speed. This research has significant implications for improving the health and safety of the elderly and people with disabilities.

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.

Abstract | Links | BibTeX

]]> Poster at DGfE 2024 https://digital-health-bonn.de/poster-at-dgfe-2024/ Tue, 27 Feb 2024 12:00:00 +0000 https://digital-health-bonn.de/?p=150 Our abstract “Sensorik am Krankenbett – Synchrone Datenakquise für Studien in der Epileptologie” will be presented as digital poster at the 62. Jahrestagung der Deutschen Gesellschaft für Epileptologie in June.

Müllers, Johannes; Greß, Hannah; Haaga, Lisa; Krüger, Björn

Sensorik am Krankenbett – Synchrone Datenakquise für Studien in der Epileptologie Conference

Clinical Epileptology, vol. 37 (Suppl 1), 2024.

Abstract | Links | BibTeX

]]> Contribution accepted for the Bernstein Confernce 2023 https://digital-health-bonn.de/diagnosing-rare-diseases-by-movement-primitive-based-classification-of-kinematic-gait-data/ https://digital-health-bonn.de/diagnosing-rare-diseases-by-movement-primitive-based-classification-of-kinematic-gait-data/#respond Tue, 08 Aug 2023 14:29:19 +0000 https://digital-health-bonn.de/?p=61 Our abstract titled “Diagnosing Rare Diseases by Movement Primitive-Based Classification of Kinematic Gait Data” was accepted as poster presentation and will be presented by our collaboration partner Jing Xu from Marburg.

Xu, Jing; Greß, Hannah; Seefried, Sabine; van Drongelen, Stefan; Schween, Raphael; Sommer, Claudia; Endres, Dominik; Krüger, Björn; Stief, Felix

Diagnosing Rare Diseases by Movement Primitive-Based Classification of Kinematic Gait Data Proceedings

Bernstein Conference, 2023.

Abstract | Links | BibTeX

@proceedings{JingXu2023,

title = {Diagnosing Rare Diseases by Movement Primitive-Based Classification of Kinematic Gait Data},

author = {Jing Xu and Hannah Greß and Sabine Seefried and Stefan van Drongelen and Raphael Schween and Claudia Sommer and Dominik Endres and Björn Krüger and Felix Stief},

url = {https://abstracts.g-node.org/conference/BC23/abstracts#/uuid/31c21041-91a0-46bd-87dc-46271501fdc0},

doi = {10.12751/nncn.bc2023.313},

year  = {2023},

date = {2023-01-10},

urldate = {2023-01-10},

booktitle = { Bernstein Conference 2023},

abstract = {Of over 6.000 known rare diseases, a considerable portion involves motor symptoms [1]. Whereas aiding diagnosis by artificial intelligence based on non-motor symptoms has shown promise [2], the potential of using movement data to this purpose has not yet been fully investigated. We therefore aim to implement a machine learning algorithm inspired by biological motor control to aid diagnosis of rare diseases by classifying data from standard kinematic clinical gait analysis.



Starting from 42-degrees-of-freedom time series of joint angles extracted from motion capture data with custom routines [3], we employ a Gaussian process-based temporal movement primitive algorithm [4] in order to reduce the data to sets of movement primitives and weight vectors that capture the essential characteristics of the gait movement. The primitives are participant (and disease) -independent and represent general human gait. The weights are participant-specific and thus contain disease-specific information. A weighted combination of the primitives can thus generate participant specific gait data. We then apply standard classification tools such as Support Vector Machines and Random Forests to the weights to distinguish the disease from the control gait. The primary goal is to reliably differentiate patients from age-matched controls in an existing data set on patients with Legg–Calvé–Perthes disease (LCPD). A secondary goal is to allow the classifier to expand the set of diseases using nonparametric methods such as the Dirichlet process.



Importantly, our movement primitive algorithm is inspired by current theories of biological motor control with a potential edge over standard algorithms in training on small case numbers. The temporal primitives are analogous to central pattern generators in the spinal cord [5], whereas the weights reflect activation of these central patterns by more central mechanisms in a hierarchical control scheme. In such a control scheme, disease-specific changes in weights may be caused directly by disease-specific influences on neural signaling, such as in the Stiff Person Syndrome [6], or indirectly through pain-avoidance in orthopedic conditions such as LCPD.



With further development, our approach holds potential for facilitating early detection and improving treatment strategies across a wide range of rare movement disorders and orthopedic conditions.},

howpublished = {Bernstein Conference},

keywords = {},

pubstate = {published},

tppubtype = {proceedings}

}

Of over 6.000 known rare diseases, a considerable portion involves motor symptoms [1]. Whereas aiding diagnosis by artificial intelligence based on non-motor symptoms has shown promise [2], the potential of using movement data to this purpose has not yet been fully investigated. We therefore aim to implement a machine learning algorithm inspired by biological motor control to aid diagnosis of rare diseases by classifying data from standard kinematic clinical gait analysis.

Starting from 42-degrees-of-freedom time series of joint angles extracted from motion capture data with custom routines [3], we employ a Gaussian process-based temporal movement primitive algorithm [4] in order to reduce the data to sets of movement primitives and weight vectors that capture the essential characteristics of the gait movement. The primitives are participant (and disease) -independent and represent general human gait. The weights are participant-specific and thus contain disease-specific information. A weighted combination of the primitives can thus generate participant specific gait data. We then apply standard classification tools such as Support Vector Machines and Random Forests to the weights to distinguish the disease from the control gait. The primary goal is to reliably differentiate patients from age-matched controls in an existing data set on patients with Legg–Calvé–Perthes disease (LCPD). A secondary goal is to allow the classifier to expand the set of diseases using nonparametric methods such as the Dirichlet process.

Importantly, our movement primitive algorithm is inspired by current theories of biological motor control with a potential edge over standard algorithms in training on small case numbers. The temporal primitives are analogous to central pattern generators in the spinal cord [5], whereas the weights reflect activation of these central patterns by more central mechanisms in a hierarchical control scheme. In such a control scheme, disease-specific changes in weights may be caused directly by disease-specific influences on neural signaling, such as in the Stiff Person Syndrome [6], or indirectly through pain-avoidance in orthopedic conditions such as LCPD.

With further development, our approach holds potential for facilitating early detection and improving treatment strategies across a wide range of rare movement disorders and orthopedic conditions.

]]> https://digital-health-bonn.de/diagnosing-rare-diseases-by-movement-primitive-based-classification-of-kinematic-gait-data/feed/ 0