Digital Health Bonn

@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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@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. }

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@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}

}

@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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@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.},

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@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.},

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@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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@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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@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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@article{s23073664,

title = {An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data},

author = {Hashim Yasin and Saba Ghani and Björn Krüger},

url = {https://www.mdpi.com/1424-8220/23/7/3664},

doi = {10.3390/s23073664},

issn = {1424-8220},

year  = {2023},

date = {2023-01-01},

journal = {Sensors},

volume = {23},

number = {7},

abstract = {In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.},

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@article{yasin-2021a,

title = {An Efficient 3D Human Pose Retrieval and Reconstruction from 2D Image-Based Landmarks},

author = {Hashim Yasin and Björn Krüger},

url = {https://www.mdpi.com/1424-8220/21/7/2415

https://digital-health-bonn.de/wp-content/uploads/2024/03/sensors-21-02415.pdf, Paper},

doi = {10.3390/s21072415},

issn = {1424-8220},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {7},

abstract = {We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of joints of the normalized 3D poses onto 2D image-planes by fully exploiting a variety of virtual cameras. With this approach, we not only transform 3D pose space to the normalized 2D pose space but also resolve the 2D-3D cross-domain retrieval task efficiently. The proposed architecture searches for poses from a MoCap dataset that are near to a given 2D query pose in a definite feature space made up of specific joint sets. These retrieved poses are then used to construct a weak perspective camera and a final 3D posture under the camera model that minimizes the reconstruction error. To estimate unknown camera parameters, we introduce a nonlinear, two-fold method. We exploit the retrieved similar poses and the viewing directions at which the MoCap dataset was sampled to minimize the projection error. Finally, we evaluate our approach thoroughly on a large number of heterogeneous 2D examples generated synthetically, 2D images with ground-truth, a variety of real in-the-wild internet images, and a proof of concept using 2D hand-drawn sketches of human poses. We conduct a pool of experiments to perform a quantitative study on PARSE dataset. We also show that the proposed system yields competitive, convincing results in comparison to other state-of-the-art methods.},

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@article{peper-2020-a,

title = {Comparing Muscle Activity and Spine Shape in Various Sitting Styles},

author = {Erik Peper and Björn Krüger and Esther Gokhale and Richard Harvey},

doi = {10.5298/1081-5937-48.3.03},

issn = {1081-5937},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Biofeedback},

volume = {48},

number = {3},

pages = {62–67},

abstract = {Back problems affect 80% of the population in the U.S. at some time during their lives, and each year 32 million Americans experience lower back pain at an estimated economic cost of $560-$635 billion. One contributing factor for back pain is posture, and more particularly, lack of awareness of dysfunctional posture. For example, many people may sit in a slouched or forward-bent position, exacerbated by poor ergonomics while sitting or, extended use of handheld digital devices while standing such as looking down at a smartphone for long periods of time. This report describes a ‘stacksitting’ technique which is one of the components of the Gokhale Method® for healthy, relaxed sitting and increased awareness of posture. The stacksitting process is illustrated with a case study where the shape of the spine and the muscle activity is analyzed in parallel during three sitting styles: slouching, arched, and stacksitting. The spine curvature was characterized by the Gokhale SpineTracker™ wearable, which consists of five sensor-units attached along the subjects’ spines that are used to plot the spinal curve on a digital device such as a smartphone, tablet or computer. SEMG recordings were made from the right upper trapezius, left upper trapezius, right mid-back, and left mid-back with a second device (Myoscan Pro sensors recorded with Biograph Procomp Infinity) while participants were seated in three postures: a slouched (forward-bent) position, an upright ‘arched’ position, and an upright ‘stacksitting’ position as trained by a Gokhale Method teacher. The case observations showed no significant difference in trapezius SEMG activity during each of the three positions. There was a slight increase in SEMG activity of the mid-back during stacksitting (1.1 µV) as compared to when slouched (0.64 µV) and, a significant increase in SEMG activity when sitting arched (4.9 µV). As expected, the spinal activity tracking device showed significant straightening of the lower spine during the stacksitting position as compared to the slouched and arched positions. The observations suggest that the stacksitting position can be taught to others in a way that allows the vertebrae to be parallel to each other with very low levels of corresponding muscle activity. In contrast, sitting in an arched or slouched position would increase asymmetrical pressures on the disks, contributing to vertebral wedging which could also contribute to spinal disk-bulging and eventual back injury. The observations suggest that proper coaching may foster a stacksitting position of the spine, which would foster a healthier posture than a slouched or arched spinal positions.},

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}

@article{Mezzarobba2020,

title = {Action Observation Plus Sonification. Biomechanical Analysis of Sit-To-Walk Performance in Patients with Parkinson's Disease and Freezing of Gait},

author = {Susanna Mezzarobba and Michele Grassi and Lorella Pellegrini and Mauro Catalan and Björn Krüger and Lara Stragapede and Paolo Manganotti and Paolo Bernardis},

doi = {https://doi.org/10.1016/j.parkreldis.2020.09.029},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Parkinsonism & Related Disorders},

volume = {80},

pages = {133–137},

abstract = {Introduction. Freezing of gait (FoG) is one of the most disabling gait disorders in Parkinson's disease (PD) reflecting motor and cognitive impairments, mainly related to dopamine deficiency. Recent studies, investigating kinematic and kinetic factors affecting gait in these patients, showed a postural instability characterized by disturbed weight-shifting, inappropriate anticipatory postural adjustment, worse reactive postural control, and a difficulty to execute complex motor task (i.e. sit-to-walk). Symptoms that are difficult to alleviate and not much responsive to Levodopa. For this reason, additional therapeutic actions based on specific therapeutic protocols may help patients in daily life. Methods. In 2018, we conducted a randomized control trial aimed to test two clinical protocols for PD patients with FoG. Protocols, conceived to improve gait, were based on learning motor exercises with the Action Observation plus Sonification (AOS) technique, and with a standard protocol centered on cue use. We found a significant improvement in the FoG questionnaire and the UPDRS III clinical scale for the AOS protocol only. We also collected biomechanical data, using the sit-to-walk task as a measure of motor performance. Results. Here we report kinetic and kinematic analysis of data showing that, when treatment effects consolidate, patients treated with AOS protocol are more efficient in merging subsequent motor tasks (sit-tostand and gait initiation) with a better dynamic balance control during the sit-to-stand. Conclusion. Promising results of the AOS protocol in reducing FoG confirm that a mobility training focused on improving weight-shifting and motor switching abilities, may have benefits for Parkinson patients with FoG.},

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tppubtype = {article}

}

@article{stollenwerk-2019a,

title = {Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device},

author = {Katharina Stollenwerk and Jonas Müller and André Hinkenjann and Björn Krüger},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/sensors-19-03625.pdf, Paper},

doi = {https://doi.org/10.3390/s19163625},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Sensors},

abstract = {Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mezzarobba2018a,

title = {Action observation plus sonification. A novel therapeutic protocol for Parkinson’s patient with freezing of gait},

author = {Susanna Mezzarobba and Michele Grassi and Lorella Pellegrini and Mauro Catalan and Björn Krüger and Giovanni Furlanis and Paolo Manganotti and Paolo Bernardis},

url = {https://www.frontiersin.org/article/10.3389/fneur.2017.00723},

doi = {10.3389/fneur.2017.00723},

issn = {1664-2295},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Frontiers in Neurology},

volume = {8},

abstract = {Freezing of gait (FoG) is a disabling symptom associated to falls, with little or no responsiveness to pharmacological treatment. Current protocols used for rehabilitation are based on the use of external sensory cues. However, cued strategies might generate an important dependence on the environment. Teaching motor strategies without cues (i.e. action observation - AO - plus sonification) could represent an alternative/innovative approach to rehabilitation that matters most on appropriate allocation of attention and lightening cognitive load. We aimed to test the effects of a novel experimental protocol to treat patients with Parkinson disease (PD) and freezing of gait, using functional, and clinical scales. The experimental protocol was based on action observation plus sonification. 12 patients were treated with 8 motor gestures. They watched 8 videos showing an actor performing the same 8 gestures, and then tried to repeat each gesture. Each video was composed by images and sounds of the gestures. By means of the sonification technique, the sounds of gestures were obtained by transforming kinematic data (velocity) recorded during gesture execution, into pitch variations. The same 8 motor gestures were also used in a second group of 10 patients; which were treated with a standard protocol based on a common sensory stimulation method. All patients were tested with functional and clinical scales before, after, at 1 month, and 3 months after the treatment. Data showed that the experimental protocol have positive effects on functional and clinical tests. In comparison with the baseline evaluations, significant performance improvements were seen in the N-FOG questionnaire, and the UPDRS (part 3 and 2). Importantly, all these improvements were consistently observed at the end, 1 month, and 3 months after treatment. No improvements effects were found in the group of patients treated with the standard protocol. These data suggest that a multisensory approach based on action observation plus sonification, with the two stimuli semantically related, could help PD patients with FoG to re-learn gait movements, to reduce freezing episodes, and that these effects could be prolonged over time.},

note = {accepted for Publication},

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pubstate = {published},

tppubtype = {article}

}

@article{iqbal-2018,

title = {A Dual-Source Approach for 3D Human Pose Estimation from Single Images},

author = {Umar Iqbal and Andreas Doering and Hashim Yasin and Björn Krüger and Andreas Weber and Juergen Gall},

url = {http://www.sciencedirect.com/science/article/pii/S1077314218300511},

doi = {https://doi.org/10.1016/j.cviu.2018.03.007},

issn = {1077-3142},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Computer Vision and Image Understanding},

volume = {172},

pages = {37–49},

abstract = {In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of large amounts of training data. Particularly, collecting a large number of unconstrained images that are annotated with accurate 3D poses is impractical. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To incorporate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zsoldos2018,

title = {Long term consistency and locationspecificity of equine gluteus medius muscleactivity during locomotion on the treadmill},

author = {Rebeka Zsoldos and Anna Vögele and Björn Krüger and Ulrike Schröder and Andreas Weber and Theresia Licka},

doi = {10.1186/s12917-018-1443-y},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {BMC Veterinary Research},

volume = {14},

number = {126},

pages = {1–10},

abstract = {Background: The equine m. gluteus medius (GM) is the largest muscle of the horse, its main movement function is the extension of the hip joint. The objective of the present study was to measure equine GM activity in three adjacent locations on GM during walk and trot on a treadmill, in order to document potential differences. Fourteen Haflinger mares were measured using surface electromyography and kinematic markers to identify the motion cycles on three occasions over 16 weeks. The electrodes were placed on left and right gluteus medius muscle over the middle of its widest part and 5 cm lateral and medial of it. For data processing, electrical activity was normalised to its maximum value and timing was normalised to the motion cycle. A Gaussian distribution approach was used to determine up to 10 modes of focussed activity, and results were analysed separately for stance and swing phase of the ipsilateral hindlimb. Results: Fair reliability was found for mean mode values (Cronbach’s alpha = 0.66) and good reliability was found for mean mode locations (Cronbach’s alpha = 0.71) over the three data collection days. The magnitude of muscle activity identified as mean mode value was much larger at trot than at walk, and mean mode value was significantly different between stance phases of walk and trot for all electrode positions (p < 0.01). The pattern of muscle activity identified as mean mode location was significantly different for walk and trot at all electrode positions, both during stance and swing phases (p < 0.001). This indicates the different timing pattern between the gaits. Results of the three electrode positions on the same muscle during each gait were not significantly different when comparing the same measurement. Conclusions: The middle of the equine GM does not show any indication of functional differentiation during walk and trot on a treadmill; this might be due to lack of segmentation as such, or due to lack of need for segmented use for these very basic main tasks of the muscle. The reliability of the sEMG measurements over several weeks was fair to good, an indication for the robustness of the methodology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{krueger2017Segmentation,

title = {Efficient Unsupervised Temporal Segmentation of Motion Data},

author = {Björn Krüger and Anna Vögele and Tobias Willig and Angela Yao and Reinhard Klein and Andreas Weber},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/MotionSegmentationCode.zip, Source Code

},

doi = {10.1109/TMM.2016.2635030},

issn = {1520-9210},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {IEEE Transactions on Multimedia},

volume = {19},

number = {4},

pages = {797–812},

abstract = {We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighborhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system’s capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{voegele2016a,

title = {Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models},

author = {Anna Vögele and Rebeka Zsoldos and Björn Krüger and Theresia Licka},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/journal.pone_.0157239.pdf, Paper},

doi = {10.1371/journal.pone.0157239},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {PLOS ONE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{effenberg2016,

title = {Movement sonification: Effects on motor learning beyond rhythmic adjustments},

author = {Alfred O. Effenberg and Ursula Fehse and Gerd Schmitz and Björn Krüger and H. Mechling},

url = {http://journal.frontiersin.org/article/10.3389/fnins.2016.00219/abstracthttps://digital-health-bonn.de/wp-content/uploads/2024/03/fnins-10-00219.pdf. Paper},

doi = {10.3389/fnins.2016.00219},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Frontiers in Neuroscience},

volume = {10},

number = {219},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{riaz-2016a,

title = {Relational Databases for Motion Data},

author = {Qaiser Riaz and Björn Krüger and Andreas Weber},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/08/Preprint-2016-Relational-Databases-for-Motion-Data.pdf, Preprint},

doi = {http://dx.doi.org/10.1504/IJICA.2016.078723},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {International Journal of Innovative Computing and Applications},

volume = {7},

number = {3},

pages = {119–134},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{riaz2015a,

title = {Motion Reconstruction Using Very Few Accelerometers and Ground Contacts},

author = {Qaiser Riaz and Tao Guanhong and Björn Krüger and Andreas Weber},

doi = {http://dx.doi.org/10.1016/j.gmod.2015.04.001},

year  = {2015},

date = {2015-01-01},

journal = {Graphical Models},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hartmann2015a,

title = {Efficient Multi-Constrained Optimization for Example-Based Synthesis},

author = {Stefan Hartmann and Elena Trunz and Björn Krüger and Reinhard Klein and Matthias B. Hullin},

url = {http://dx.doi.org/10.1007/s00371-015-1114-y},

doi = {10.1007/s00371-015-1114-y},

year  = {2015},

date = {2015-01-01},

journal = {The Visual Computer / Proc. Computer Graphics International (CGI 2015)},

volume = {31},

number = {6-8},

pages = {893–904},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{riaz2015c,

title = {One Small Step for a Man: Estimation of Gender, Age, and Height from Recordings of One Step by a Single Inertial Sensor},

author = {Qaiser Riaz and Anna Vögele and Björn Krüger and Andreas Weber},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/riaz2015c.pdf, Paper},

doi = {10.3390/s151229907},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Sensors},

volume = {15},

number = {12},

pages = {31999–32019},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{riaz2015b,

title = {A Relational Database for Human Motion Data},

author = {Qaiser Riaz and Björn Krüger and Andreas Weber},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/riaz2015a.pdf, Paper},

doi = {10.1007/978-3-319-21413-9_17},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Lecture Notes in Computer Science, Computational Science and Its Applications - ICCSA 2015},

volume = {9159},

pages = {234–249},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yasin2014a,

title = {Motion Tracking, Retrieval and 3D Reconstruction from Video},

author = {Hashim Yasin and Björn Krüger and Andreas Weber},

url = {http://www.sersc.org/journals/IJMUE/vol9_no2_2014/27.pdf},

issn = {1975-0080},

year  = {2014},

date = {2014-01-01},

journal = {International Journal of Multimedia and Ubiquitous Engineering (IJMUE)},

volume = {9},

number = {2},

pages = {265–280},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zsoldos2014a,

title = {From maturity to old age: tasks of daily life require a different muscle use in horses},

author = {Rebeka Zsoldos and Björn Krüger and Theresia Licka},

url = {http://wageningenacademic.metapress.com/content/a016363755605421/},

doi = {10.3920/CEP140001},

issn = {1755-2540 (Print) 1755-2559 (Onl},

year  = {2014},

date = {2014-01-01},

journal = {Comparative Exercise Physiology},

volume = {10},

number = {2},

pages = {75–88},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{krueger2014b,

title = {Sleep Detection using a Depth Camera},

author = {Björn Krüger and Anna Vögele and Lukas Herwartz and Thomas Terkatz and Andreas Weber and Carmen Garcia and Ingo Fietze and Thomas Penzel},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Computational Science and Its Applications (ICCSA) [accepted for publication]},

journal = {Lecture Notes in Computer Science, ICCSA 2014, Part I},

volume = {LNCS 8579},

pages = {824–835},

address = {Minho, Guimaraes, Portugal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{krueger2014c,

title = {Sleep Detection using De-Identified Depth Data},

author = {Björn Krüger and Anna Vögele and Marouane Lassiri and Lukas Herwartz and Thomas Terkatz and Andreas Weber and Carmen Garcia and Ingo Fietze and Thomas Penzel},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/krueger2014c.pdf, Paper},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Journal of Mobile Multimedia},

volume = {10},

number = {3&4},

pages = {327–342},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bernard2013,

title = {MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation},

author = {Jürgen Bernard and Nils Wilhelm and Björn Krüger and Thorsten May and Tobias Schreck and Jörn Kohlhammer},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/bernard2013.pdf, Paper},

doi = {10.1109/TVCG.2013.178},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {IEEE Transactions on Visualization and Computer Graphics},

volume = {19},

number = {12},

pages = {2257-2266},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tautges-2011-MotionReconstruction,

title = {Motion Reconstruction Using Sparse Accelerometer Data},

author = {Jochen Tautges and Arno Zinke and Björn Krüger and Jan Baumann and Andreas Weber and Thomas Helten and Meinard Müller and Hans-Peter Seidel and Bernd Eberhardt},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/2011-tautges-motionreconstruction.pdf, Paper preprint},

doi = {10.1145/1966394.1966397},

issn = {0730-0301},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {ACM Trans. Graph.},

volume = {30},

number = {3},

pages = {18:1–18:12},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{krueger-2008-multi,

title = {Multi-Mode Tensor Representation of Motion Data},

author = {Björn Krüger and Jochen Tautges and Meinard Müller and Andreas Weber},

url = {https://digital-health-bonn.de/wp-content/uploads/2024/03/krueger-2008-multi.pdf, Paper},

issn = {1860-2037},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {Journal of Virtual Reality and Broadcasting},

volume = {5},

number = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}