bluuebunny/crossref_metadata_2025_split huggingface dataset:
Granular Jamming Gripper for an Ankle Rehabilitation Robot
Authors: Halouani Nahed , Shah Nazar Peiman , Pott Peter P. 
Published: December 2024

Abstract
Rehabilitation exercises are a crucial component in the treatment of ankle injuries. With the increasing demand on physiotherapists, many advances have been made in robotassisted ankle rehabilitation, which require an attachment mechanism of the foot or leg on the device. However, securely holding the ankle may cause physical discomfort and for the patient to feel constrained to the mechanism. This paper introduces a gripper of the plantar surface of the foot for ankle rehabilitation employing a universal Granular Jamming Gripper (GJG). It consists of a granule filled membrane that allows the patient’s foot to be immersed in it when it is filled with air and hardens around the foot when vacuum is applied. The paper also investigates the optimal filling percentage of the membrane, membrane thickness and the effect of granule types of different physical properties (ground coffee, table salt, coffee/salt mixture, and granular sugar) on the gripping ability and overall function of the GJG. Tensile and immersion tests demonstrated the suitability of the GJG for gripping a foot model as well as the increased gripping function with a 0.25 mm membrane filled to 60 % with granulated sugar.
Quantitative Convolutional Neural Network Based Multi-Phase XRD Pattern Analysis
Authors: Höfer Hawo H. , Orth André , Schweidler Simon , Breitung Ben , Aghassi-Hagmann Jasmin , Reischl Markus 
Published: December 2024

Abstract
X-ray diffraction (XRD) is commonly used to analyze phase compositions of crystalline samples. Medical applications include the analysis of biotechnological materials and gall- and kidney stones, where composition can inform pathology assessment. XRD analysis methods like Rietveld refinement requires expert knowledge, and multi-phase sample analysis is especially challenging and time consuming. Largescale medical and biotechnological experiments can therefore be hindered by the need to perform analysis tasks using XRD. Here, we present preliminary results on an automated convolutional neural network (CNN) based method for sample composition analysis using XRD patterns. It can aid experts’ analysis using initial estimations, and enable basic judgements for non-experts. Furthermore, we confirm the intuitive notion that analysis performance degrades with sample complexity through systematic investigation using a synthetic dataset.
Non-invasive vital parameter detection using neuromorphic cameras
Authors: Huhs Niklas , Kalashtari Niloofar , Kraitl Jens , Hornberger Christoph , Simanski Olaf 
Published: December 2024

Abstract
The non-invasive detection of vital parameters is gaining significance in the medical field through the use of various established and novel camera technologies and the use of artificial intelligence for data processing. A recently emerging type of imaging sensor, the neuromorphic camera, mimics the way the human eye perceives changes in its vision by responding to local changes in brightness for each pixel. The use of these neuromorphic cameras in the medical field is mostly unexplored and offers new possibilities. This research experiment is designed as a proof of concept for the use of neuromorphic cameras to detect the heart rate and respiration rate of a person by monitoring movement and subtle vibrations of the abdominal and chest area. The experimental setup consisted of a DVXplorer camera positioned 30 cm from a healthy person, directed at the abdominal and chest area. The respiration rate was detected with a mean absolute error of 0.66 bpm. The heart rate was detected with an accuracy of 95 % and a mean absolute error of less than 2 bpm. This shows the possibility to detect the heart rate and respiration rate of a person reliably, using neuromorphic cameras under controlled conditions. Future research will focus on improving reliability under more challenging environmental circumstances like varying light conditions and movement of the patient.
Development of a measurement setup to determine the frictional properties of tissuemimicking materials for vascular models
Authors: Karcher Alisa , Schäfer Jenny , Cattaneo Giorgio , Sanchez Daniela 
Published: December 2024

Abstract
In this study, a measurement setup was developed to determine the frictional properties between tissue-mimicking and implant materials. Nitinol and Polytetrafluoroethylene (PTFE) were selected as implant materials, while Polyvinyl alcohol (PVA) hydrogel and silicone were chosen as tissue-mimicking substrates and compared to porcine aortic tissue. The results showed a considerably lower friction coefficient between nitinol and all substrates compared to PTFE. PVA hydrogel showed a lower friction coefficient than silicone in contact to all implant materials, but still a higher coefficient compared to aortic tissue. Moreover, parameters such as PVA concentration and aging through storage influenced the frictional properties of PVA hydrogel. Finally, feasibility of the novel measurement setup to measure friction under physiological conditions as well as a comparison with previous studies are discussed.
Influence of TAVR-dilatation concepts on hydrodynamic properties under pulsatile flow
Authors: Kaule Sebastian , Stiehm Michael , Siewert Stefan , Öner Alper , Schmitz Klaus-Peter 
Published: December 2024

Abstract
Transcatheter aortic valve implantation (TAVI) for aortic valve stenosis can be seen as a disruptive technology that has become the standard treatment for previously inoperable patients over the past 15 years. A key component of the development process for new transcatheter aortic valve replacements (TAVR) is the hydrodynamic testing as part of real-time functional testing. Although, fundamental requirements, necessary testing methods, as well as functional environments, physiological and pathophysiological stress situations, are defined, resulting testing parameter ranges are only estimated. For this reason, two clinically established TAVR were tested in different parameter settings representing different characteristic load situations on a patient. In fact, the two TAVR with different dilatation concepts were tested under different cardiac outputs (CO) and the resulting effective orifice area (EOA), the closing and the leakage volume was measured and evaluated according to ISO 5840-3:2021. Regarding the two TAVR designs, it can be shown that the high radial force of the Lotus Valve contributes to an improvement in the sealing effectiveness of the TAVR. This is because the annulus can be pushed outward by the stent resulting in a better fit between the TAVR and the vessel wall. Additionally, the lamellar skirt of the Lotus Valve also appears to contribute to improved sealing. In the presented experiments, the smooth abluminal- mounted pericardial skirt of the Evolut PRO bioprosthesis does not seem to contribute to additional sealing. The developed test procedure aims to contribute to the establishment of new in vitro standards. Future work must involve applying the testing methods to additional clinically established TAVR as well as new developments.
Empirical Study of Magnet Distance on Magneto-Mechanical Resonance Frequency
Authors: Knopp Tobias , Mohn Fabian , Foerger Fynn , Thieben Florian , Hackelberg Niklas , Faltinath Jonas , Tsanda Artyom , Boberg Marija , Möddel Martin 
Published: December 2024

Abstract
Determining the position and orientation of a medical instrument is essential for accurate procedures in endoscopy, surgery, and vascular interventions. Recently, a novel sensor based on torsional pendulum-like magneto-mechanical motion has been proposed. This sensor is passive, wireless and inductively coupled to a transmit-receive coil array. This setup allows the determination of all 6 degrees of freedom using the characteristic resonance of the sensor. Additional physical quantities such as temperature and pressure can be measured based on the frequency of the sensor, which mainly depends on the distance between the two involved permanent magnets. In this study, we analyze a sensor composed of two magnetic cylinders with variable magnet-to-magnet distance and a basic physical model based on a dipole assumption. Experimental analysis of the resonance frequency and comparison with the model values show both qualitative and quantitative agreement with an average relative error of only 0.8 %. This validates the implemented model and shows the suitability of our magneticmechanical resonator made from cylindrical permanent magnets for sensing applications.
A setup for live AI support in interventional radiology
Authors: Komposch Jan , Malzacher Till , Baumgärtner Timo , Braun Michael , Roßkopf Johannes , Franz Alfred M. , Schmitz Bernd 
Published: December 2024

Abstract
Artificial intelligence (AI) has the potential to support time-critical stroke treatment. In a previous study we demonstrated the feasibility of deep learning based automatic classification for thrombus detection during thrombectomies, a catheter-guided procedure to remove occlusions of cerebral vessels. However, this method has yet to be tested during a live intervention. In this work, we present a setup to integrate AI based support in an angiography suite. A classification PC was connected to the angiography by means of a real-time video connection as well as a research interface for control signals. We found that video conversion in real-time does not affect the classification result in comparison to offline classification of DICOM data. Analyzing 50 video streams of previous cases, the system could classify digital-subtraction angiography (DSA) sequences within 13.3 seconds on average. This processing time can further be reduced to an average of 7.9 seconds with GPU acceleration. Additionally, the system successfully classified two DSA sequences acquired during live thrombectomy, identifying the presence of thrombi in less than 5 seconds. So far, the classification result has only been displayed in the control room of the angiography suite to demonstrate feasibility. In the outlook, however, we also discuss how the result can be displayed directly on the angiography screen.
On Stair Walk Recognition Using a Single Magnetometer-free IMU and Deep Learning
Authors: Kuhlgatz Timo , Jordine Marco , Lehmann Dustin , Seel Thomas 
Published: December 2024

Abstract
Human activity recognition (HAR) is an expanding area of research. Although sensors are becoming more readily available, there is a trend toward minimizing the number of associated sensors for better applicability. Neural networks are often employed for HAR, as they can identify movement patterns within data. To optimize the amount of useful information provided to the network, feature extraction methods are commonly applied. However, these feature methods complicate the data processing pipeline, and thus are less applicable to continuous real-time applications. In this work, we investigate the impact of using quaternions as input features for an LSTM network on HAR, specifically focusing on level walking and stair climbing activities, while also considering the inference time. We demonstrate that combining quaternions and raw IMU data, i. e., acceleration and angular rates significantly enhances classification accuracy without adversely affecting the inference time. By additionally adding an approximate estimate of the vertical position change to the input data, the classification accuracy is further improved to a value of 96.18%.
Advancement and evaluation of a medical organization device
Authors: Langer Ferdinand , Welte Korinna , Widmer Jette , Schmid Peter , Maier Thomas , Trick Michael 
Published: December 2024

Abstract
In anaesthesia and intensive care medicine, managing the multitude of signal and substance lines connected to patients presents challenges, risking dislocations, incorrect treatment, and increased workload for healthcare professionals. Existing solutions for signal and substance line management have seen limited acceptance, lacking universality and flexibility. Addressing these challanges, the “intelligent Medical Organization Device” (iMOD) was developed as a modular tool. iMOD, evaluated through simulator training, demonstrated positive results in intuitiveness and acceptance. Feedback highlighted areas for improvement, focusing on line insertion and fixation strength. The organization device may be a component in reducing treatment time and costs, increasing patient safety and improving the satisfaction of patients and healthcare professionals.
FDM technology for the prototyping of polymer stents
Authors: Lebahn Kerstin , Luft David , Fiedler Nicklas , Tautorat Carsten , Grabow Niels 
Published: December 2024

Abstract
Fused deposition modelling (FDM) technology allows the use of a wide range of thermally processable biocompatible or biodegradable polymers. Biomedical research is currently focusing on the development of 3D printed stents that can be biodegradable, as well as drugeluting. In this proof-of-concept study, a cylindrical substrate was integrated into a commercial FDM printer to establish a process for stent printing. Stents printed on a planar substrate were compared with those printed on the cylindrical substrate. The typical stepped structure along the stent struts on the planar print bed caused by layered printing is avoided with the cylindrical substrate.
A normalized quality of life approach for regulatory approval of medical devices
Authors: Leuchter Matthias , Philipp Mark , Siewert Stefan , Schafmayer Clemens , Schmitz Klaus-Peter , Kaule Sebastian 
Published: December 2024

Abstract
Patient-reported outcomes (PROs) are used in clinical trials to provide valuable evidence on the impact of treatment on patients’ symptoms, function and quality of life. High-quality PRO data from trials can inform shared decisionmaking, regulatory and economic analyses and health policy. The heterogeneity between the patient groups and the associated differences in morbidity and quality of life can be taken into account by standardization to the general population. As a result, our study shows that despite the retrospective design with the corresponding level of evidence, mesh repair of a ventral hernia leads to an equivalent result compared to tissue hernia surgery.
A short review of modification techniques for titanium dental implant surfaces
Authors: Oezcelik Sude , Haeger Andreas 
Published: December 2024

Abstract
In the world of dental implants, the essential determinant of success lies in the phenomenon of osseointegration, wherein titanium implants exhibit the ability for robust integration with the surrounding bone and tissue. Beyond the inherent qualities of titanium which make it an attractive candidate as an implant material, attention has shifted towards its’ tailored surface modifications. These methods help the implant manufacturers to modify the implant surface for successful osseointegration and higher chances of enduring implantation. This review explores different surface modification methods and their impact on the dental implant success.
The tribological behaviour of titanium alloys suitable for dental implants: A short review
Authors: Roegnitz Alexander , Haeger Andreas 
Published: December 2024

Abstract
Titanium and its alloys play a vital role in dental implantology, providing a stable foundation for replacement teeth and restoring oral function effectively. Titanium alloys, particularly CP-Ti and Ti-6Al-4V, are widely utilized due to their biocompatibility, corrosion resistance and mechanical properties. However, concerns arise regarding the release of cytotoxic elements and stress shielding effects. Addressing these concerns requires advancements in material design and tribological properties. Research focuses on developing low modulus β-type Ti-based alloys with improved wear resistance and biocompatibility, incorporating elements like niobium, tantalum or zirconium. This review explores the tribological implications of these advancements, focussing on the implantabutment interface, emphasizing the need for optimized titanium alloys for dental implant applications.
Tissue Puncture Event Detection in Needle Procedures using Vibroacoustic Signals - ResNet optimised Phantom Results
Authors: Serwatka Witold , Rzepka Dominik , Oran Hamza , Steeg Katharina , Krombach Gabriele , Stefanski Juliusz , Heryan Katarzyna , Friebe Michael 
Published: December 2024

Abstract
Vibroacoustic signals generated by the interaction of moving clinical devices, e.g. an aspiration or biopsy needle, with different tissues creates a distinct signal. These signals can be received via a dedicated audio sensor and subsequently analysed with the potential to provide information about location, tissue characterisation, and event classification. With that it could also be used as an additional and complementary guidance tool particularly for future robotic assisted procedures. In our laboratory research we used different phantoms with animal and artificial tissues, audio pre-processing, and subsequent training and optimisation of a ResNet model with a tissue event detection F1 score of 95.3% when compared to video based annotation tool. This result is very encouraging, as several possible improvements have been identified that will be implemented in the next research steps together with a robot assisted insertion and an automatic video annotation algorithm.
Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets
Authors: Steinbrinker Tabea , Spicher Nicolai 
Published: December 2024

Abstract
Wearable electrocardiography monitors, e.g. embedded in textile shirts, offer new approaches in diagnosis but suffers upon limited computational capacities. Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. For each type of noise a dictionary is built using K-singular value decomposition. This iterative method alternates between finding a sparse representation for every training signal and then updating every atom of the dictionary on its own. A sparse representation is found using the orthogonal matching pursuit algorithm. The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm. Electrocardiography data stems from synthetically-generated signals as well as the freely-available Brno University of Technology ECG Quality Database. Noise is added to the signals using the MIT-BIH Noise Stress Database. Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. Moreover, a small number of training data is sufficient for satisfying results which indicates the suitability of the method for wearable hardware with low memory and power specifications.
Approach for decontaminating large rooms by dry fogging
Authors: Buhl Sebastian , Stich Alexander , Bulitta Clemens 
Published: December 2024

Abstract
Many factors can negatively affect the effectiveness of cleaning and disinfection measures. Due to this situation, the Technical University of Amberg-Weiden in cooperation with aquagroup AG investigated a dry fogging technology as an automated system for decontamination of limited volumes. Preliminary tests in smaller rooms had already shown positive results in this context. An upscaling of the method would therefore be of interest, as it could be used to decontaminate rooms that are difficult or very costly to reprocess due to their size or layout. In order to evaluate the use of this system in operating theaters or larger rooms, a test setup was established in an aquagroup AG painting hall. In addition, thereby the effectiveness of the method can be evaluated in unprepared and unsealed rooms with high ventilation. Our results show a maximum germ reduction of over 5.7 log levels on tiles and 5.2 log levels on plastic. If further studies confirm these initial positive results, the dry fogging we are investigating could be another option for cleaning or disinfecting large areas quickly and effectively.
EEG-Based User Identification using Machine Learning and Deep Learning Approaches
Authors: Sushma S. , Venkat S. , Mohanavelu K. , Fredo Jac A. R. , Bobby T. Christy 
Published: December 2024

Abstract
In recent years, Electroencephalogram (EEG) based user authentication systems have gained significant interest as an innovative approach for identity verification. EEGs are considered to be a novel biometric attribute due to the individuality of each person’s cerebral activity patterns. This work explores the feasibility and efficiency of utilizing EEG signals, generated in response to emotional stimuli, for user authentication applications, by implementing Machine Learning (ML) and Deep Learning (DL) approaches. Support Vector Machine (SVM), Random Forest (RF) classifier and 1D Convolution Neural Network (CNN) were employed to evaluate and compare the performance of EEG-based user authentication for two publicly available EEG datasets, namely DEAP and DENS database. The performance of EEGbased user authentication was significantly high in LAHV emotional state for DENS dataset, achieving an accuracy of 99.2 % and 92.59 % with SVM and modified 1D CNN, respectively.
A virtual reality experimental setup to explore volitional control in lower limb prostheses
Authors: Voß Matthias , Quellhorst Marie , Beckerle Philipp 
Published: December 2024

Abstract
Active leg prostheses can provide individuals with an amputation with motion support and thereby enable better participation in societal life. However, their versatility of use is still constrained, particularly with respect to volitional control by the user. Promising volitional control methods like employing muscle and joint model simulations to mimic human motor control are actively researched. However, experimental paradigms currently either include costly hardware setups or are limited to very simplistic virtual setups. For rapid control prototyping and facilitating the involvement of healthy participants, we propose an immersive virtual reality environment, focusing on ankle prostheses. Based on a theoretical discussion and a pilot study, we outline the potential of this method for agile development of lower limb volitional controllers.
Institute of Technical Medicine (ITeM), Hochschule Furtwangen University (HFU), Germany
Authors: Winkler Simon , Sackmann Benjamin , Priwitzer Barbara , Lauxmann Michael 
Published: December 2024

Abstract
Machine learning algorithms and neural networks have recently been used for the classification of middle ear disorders using wideband acoustic immittance and wideband tympanometry data. This study applies the extreme gradient boosting (XGB) classifier, trained on simulated WAI data, to classify real measured data for normal, otosclerotic, and disarticulated ears. The achieved macro recall of 82 % is comparable to other approaches trained with real measurement data. The interpretability methods LIME and SHAP are used to quantify each feature’s contribution, both revealing energy reflectance between 600-800 Hz as a key feature for all classes. The key feature identified matches the differences that can be visually observed in the training and test data. However, the obtained feature contributions don’t provide enough distinguishable information to recognise incorrect or uncertain classifications.