@article{MendelRauberSouzaJretal., author = {Mendel, Robert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation}, series = {Computers in Biology and Medicine}, volume = {154}, journal = {Computers in Biology and Medicine}, number = {March}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2023.106585}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-57790}, pages = {13}, abstract = {Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student's prediction. The combined outputs of the students' prediction with the teachers' correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.}, language = {en} } @article{EbigboMendelRueckertetal., author = {Ebigbo, Alanna and Mendel, Robert and R{\"u}ckert, Tobias and Schuster, Laurin and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Mende, Matthias and Steinbr{\"u}ck, Ingo and Faiss, Siegbert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Deprez, Pierre and Oyama, Tsuneo and Takahashi, Akiko and Seewald, Stefan and Sharma, Prateek and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of Artificial Intelligence: A pilot Study}, series = {Endoscopy}, volume = {53}, journal = {Endoscopy}, number = {09}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-1311-8570}, pages = {878 -- 883}, abstract = {Background and aims: The accurate differentiation between T1a and T1b Barrett's cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.}, subject = {Maschinelles Lernen}, language = {en} } @article{SouzaJrPachecoPassosetal., author = {Souza Jr., Luis Antonio de and Pacheco, Andr{\´e} G.C. and Passos, Leandro A. and Santana, Marcos C. S. and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett's esophagus}, series = {Neural Computing and Applications}, volume = {36}, journal = {Neural Computing and Applications}, publisher = {Springer}, address = {London}, doi = {10.1007/s00521-024-09615-z}, pages = {10445 -- 10459}, abstract = {Limitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett's esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95\% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer.}, subject = {Deep Learning}, language = {en} } @article{SouzaJrPalmMendeletal., author = {Souza Jr., Luis Antonio de and Palm, Christoph and Mendel, Robert and Hook, Christian and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Weber, Silke A. T. and Papa, Jo{\~a}o Paulo}, title = {A survey on Barrett's esophagus analysis using machine learning}, series = {Computers in Biology and Medicine}, volume = {96}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2018.03.014}, pages = {203 -- 213}, abstract = {This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{PassosSouzaJrMendeletal., author = {Passos, Leandro A. and Souza Jr., Luis Antonio de and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's esophagus analysis using infinity Restricted Boltzmann Machines}, series = {Journal of Visual Communication and Image Representation}, volume = {59}, journal = {Journal of Visual Communication and Image Representation}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2019.01.043}, pages = {475 -- 485}, abstract = {The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{SouzaJrHookPapaetal., author = {Souza Jr., Luis Antonio de and Hook, Christian and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Barrett's Esophagus Analysis Using SURF Features}, series = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-54345-0_34}, pages = {141 -- 146}, abstract = {The development of adenocarcinoma in Barrett's esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being "cancerous", the other half as "non-cancerous". Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{SouzaJrEbigboProbstetal., author = {Souza Jr., Luis Antonio de and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Mendel, Robert and Palm, Christoph}, title = {Barrett's Esophagus Identification Using Color Co-occurrence Matrices}, series = {31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018}, booktitle = {31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018}, doi = {10.1109/SIBGRAPI.2018.00028}, pages = {166 -- 173}, abstract = {In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett'sEsophagus (BE)and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basisunction (SVM-RBF) and Bayesian classifier supports the contextof automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73\% for positive adenocarcinoma identification and specificity close to 77\% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67\% and specificity around to76\%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step.}, language = {en} } @inproceedings{SouzaJrAfonsoPalmetal., author = {Souza Jr., Luis Antonio de and Afonso, Luis Claudio Sugi and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's Esophagus Identification Using Optimum-Path Forest}, series = {Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niter{\´o}i, Rio de Janeiro, Brazil, 2017, 17-20 October}, booktitle = {Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niter{\´o}i, Rio de Janeiro, Brazil, 2017, 17-20 October}, doi = {10.1109/SIBGRAPI.2017.47}, pages = {308 -- 314}, abstract = {Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the well known Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @misc{MendelSouzaJrRauberetal., author = {Mendel, Robert and Souza Jr., Luis Antonio de and Rauber, David and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision}, series = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, journal = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-33197-9}, doi = {10.1007/978-3-658-33198-6_43}, pages = {178}, abstract = {Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.}, subject = {Deep Learning}, language = {en} } @book{LauerJanusch, author = {Lauer, Norina and Janusch, Beate}, title = {Sprechapraxie im Kindes- und Erwachsenenalter}, edition = {3., vollst. {\"u}berarb. Aufl.}, publisher = {Thieme}, address = {Stuttgart}, isbn = {9783132447004}, abstract = {Von den modelltheoretischen Grundlagen hin zu konkreten diagnostischen M{\"o}glichkeiten, diese komplexe Symptomatik zu erfassen; Zahlreiche Therapieans{\"a}tze - verst{\"a}ndlich, pr{\"a}zise und gezielt in der Praxis anwendbar; Wertvoller Begleiter f{\"u}r Lehrende, Studierende sowie praktisch t{\"a}tige Therapeutinnen und Therapeuten}, subject = {Sprechst{\"o}rung}, language = {de} } @book{LauerBirnerJanusch, author = {Lauer, Norina and Birner-Janusch, Beate}, title = {Sprechapraxie im Kindes- und Erwachsenenalter}, edition = {2., vollst. {\"u}berarb. Aufl.}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/b-002-40819}, subject = {Logop{\"a}die}, language = {de} } @article{WagnerKranzKrenkeletal., author = {Wagner, Maria Stella and Kranz, Michael and Krenkel, Lars and Pointner, Daniel and Foltan, Maik and Lubnow, Matthias and Lehle, Karla}, title = {Computer based visualization of clot structures in extracorporeal membrane oxygenation and histological clot investigations for understanding thrombosis in membrane lungs}, series = {Frontiers in Medicine}, journal = {Frontiers in Medicine}, number = {11}, editor = {Becatti, Matteo}, publisher = {Frontiers}, doi = {10.3389/fmed.2024.1416319}, abstract = {Extracorporeal membrane oxygenation (ECMO) was established as a treatment for severe cardiac or respiratory disease. Intra-device clot formation is a common risk. This is based on complex coagulation phenomena which are not yet sufficiently understood. The objective was the development and validation of a methodology to capture the key properties of clots deposed in membrane lungs (MLs), such as clot size, distribution, burden, and composition. One end-oftherapy PLS ML was examined. Clot detection was performed using multidetector computed tomography (MDCT), microcomputed tomography (μCT), and photography of fiber mats (fiber mat imaging, FMI). Histological staining was conducted for von Willebrand factor (vWF), platelets (CD42b, CD62P), fibrin, and nucleated cells (4′, 6-diamidino-2-phenylindole, DAPI). The three imaging methods showed similar clot distribution inside the ML. Independent of the imaging method, clot loading was detected predominantly in the inlet chamber of the ML. The μCT had the highest accuracy. However, it was more expensive and time consuming than MDCT or FMI. The MDCT detected the clots with low scanning time. Due to its lower resolution, it only showed clotted areas but not the exact shape of clot structures. FMI represented the simplest variant, requiring little effort and resources. FMI allowed clot localization and calculation of clot volume. Histological evaluation indicated omnipresent immunological deposits throughout the ML. Visually clot-free areas were covered with leukocytes and platelets forming platelet-leukocyte aggregates (PLAs). Cells were embedded in vWF cobwebs, while vWF fibers were negligible. In conclusion, the presented methodology allowed adequate clot identification and histological classification of possible thrombosis markers such as PLAs.}, language = {en} } @unpublished{DotterVetterHaugetal., author = {Dotter, Caroline and Vetter, Miriam and Haug, Sonja and Weber, Karsten}, title = {Determinants of Electricity Expenditure by Private Households. Analysis of the 2018 Survey of Income and Expenditure for Germany}, publisher = {SSRN - Elsevier}, doi = {10.2139/ssrn.4815428}, abstract = {Determinants of household electricity consumption measured by smart meters found by the authors in a scoping review were analyzed for the example of Germany utilizing the 2018 Survey of Income and Expenditure. All variables identified in the scoping review were covered in the survey (number and type of appliances, sociodemographic, and dwelling-related aspects). One can therefore use this large representative data set to test these relationships for German households. Expenditure on electricity is considered an indicator of household electricity consumption. The determinants show weak to moderate correlations with energy expenditure in bivariate analyses. The multivariate analysis shows effects of household-specific, dwelling-related, and appliance-specific factors. Models considering only one aspect overestimate this effect. Thus, all three aspects should be considered simultaneously when explaining residential electricity consumption. The largest effects are found for electricity as the main energy source for heating, the number of household members, as well as their presence at home. While household structure plays an important part in explaining residential energy consumption, dwelling and appliance-related aspects influence it as well. The latter aspects may be influenced by appropriate policy measures.}, language = {en} } @inproceedings{CurrleHaug, author = {Currle, Edda and Haug, Sonja}, title = {Acceptance of Telepresence Robotics, Telecare and Teletherapy Among Stroke Patients, Relatives and Therapy Staff}, series = {dHealth 2024: proceedings of the 18th annual conference on Health Informatics meets Digital Health conference}, booktitle = {dHealth 2024: proceedings of the 18th annual conference on Health Informatics meets Digital Health conference}, publisher = {IOS Press}, isbn = {978-1-64368-517-5}, issn = {0926-9630}, doi = {10.3233/SHTI240005}, pages = {15 -- 21}, abstract = {Background: Stroke as a cause of disability in adulthood causes an increasing demand for therapy and care services, including telecare and teletherapy. Objectives: Aim of the study is to analyse the acceptance of telepresence robotics and digital therapy applications. Methods: Longitudinal study with a before and after survey of patients, relatives and care and therapy staff. Results: Acceptance of the technology analysed is high in all three groups. Although acceptance among patients declined in parts of the cases in the second survey after having used telerobotics, all in all approval ratings remained high. With regard to patients no significant correlation was found between the general technology acceptance and the acceptance of use of telerobotics. Conclusion: Accepted new telecare and teletherapies can be offered with the help of telepresence robotics. This requires knowledge of and experience with the technology.}, language = {en} } @incollection{WeberKleine, author = {Weber, Karsten and Kleine, Nadine}, title = {Normierung, Regulierung, Governance: Wie, von wem und mit welchen Mitteln kann der Einsatz K{\"u}nstlicher Intelligenz gesellschaftlich gestaltet werden?}, series = {K{\"u}nstliche Intelligenz, Mensch und Gesellschaft: Soziale Dynamiken und gesellschaftliche Folgen einer technologischen Innovation}, booktitle = {K{\"u}nstliche Intelligenz, Mensch und Gesellschaft: Soziale Dynamiken und gesellschaftliche Folgen einer technologischen Innovation}, editor = {Heinlein, Michael and Huchler, Norbert}, publisher = {Springer}, isbn = {978-3-658-43521-9}, doi = {10.1007/978-3-658-43521-9_8}, pages = {179 -- 198}, abstract = {K{\"u}nstliche Intelligenz (KI) stellt eine Schl{\"u}sseltechnologie des gesellschaftlichen Wandels im 21. Jahrhundert dar. Mittlerweile werden zahlreiche technologische Anwendungen genutzt, die auf maschinellem Lernen und den damit verbundenen M{\"o}glichkeiten der Datensamm¬lung, -nutzung und -verwertung aufbauen. Indem KI große Datenmengen beherrschbar und verborgene Muster und Zusammenh{\"a}nge sichtbar macht, wird vieles schneller, einfacher und effizienter - sei es im Alltag, in der Arbeit oder in Organisationen. Offen bleibt jedoch nach wie vor die Frage, welche tiefgreifenden und teilweise latenten Folgen f{\"u}r den Menschen als soziales Wesen und das gesellschaftliche Zusammenleben mit dem Einsatz und der Entwick¬lung von KI verbunden sind. Wie wandelt sich das Verh{\"a}ltnis von Mensch und Technik durch KI und wie ist dieser Wandel zu bewerten? Welche Chancen, aber auch Risiken er{\"o}ffnen sich durch den Einsatz und die Entwicklung von KI f{\"u}r Mensch und Gesellschaft? Welchen Grenzen unterliegt der Wandel und welche Gestaltungsm{\"o}glichkeiten bieten sich? Und nicht zuletzt: Was und wer bestimmt die Entwicklungspfade, die KI nimmt - mit welchen Folgen und f{\"u}r wen?}, language = {de} } @unpublished{FoerstlAdlerSuessetal., author = {F{\"o}rstl, Nikolas and Adler, Ina and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Technologies for Evaluation of Pelvic Floor Functionality: A Systematic Review}, publisher = {Center for Open Science}, doi = {10.31219/osf.io/dcqyg}, abstract = {Pelvic floor dysfunction is a common problem in women and has a negative impact ontheir quality of life. The aim of this review was to provide a general overview of the current state oftechnology used to assess pelvic floor functionality. It also provides literature research of the phys-iological and anatomical factors that correlate with pelvic floor health. The systematic review wasconducted according to the PRISMA guidelines. PubMed, ScienceDirect, Cochrane Library andIEEE databases were searched for publications on sensor technology for the assessment of pelvicfloor functionality. Anatomical and physiological parameters were identified through a manualsearch. In the systematic review 115 publications were included. 12 different sensor technologieswere identified. Information on the obtained parameters, sensor position, test activities and subjectcharacteristics were prepared in tabular form from each publication. 16 anatomical and physiologi- cal parameters influencing pelvic floor health were identified in 17 published studies and rankedfor their statistical significance. Taken together, this review could serve as a basis for the develop-ment of novel sensors which could allow for quantifiable prevention and diagnosis, as well as par-ticularized documentation of rehabilitation processes related to pelvic floor dysfunctions.}, language = {en} } @techreport{GreinerCurrleBahretal., type = {Working Paper}, author = {Greiner, Nina and Currle, Edda and Bahr, Gudrun and Weber, Karsten}, title = {Die Rekrutierung von Proband*innen im Projekt DeinHaus 4.0 Oberpfalz - Prozess, Herausforderungen und Bewertung}, series = {Telepr{\"a}senzroboter f{\"u}r die Pflege und Unterst{\"u}tzung von Schlaganfallpatientinnen und -patienten (TePUS) im Regierungsbezirk Oberpfalz: DeinHaus 4.0 : Arbeitspapier 2.09}, journal = {Telepr{\"a}senzroboter f{\"u}r die Pflege und Unterst{\"u}tzung von Schlaganfallpatientinnen und -patienten (TePUS) im Regierungsbezirk Oberpfalz: DeinHaus 4.0 : Arbeitspapier 2.09}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule (OTH) Regensburg}, doi = {10.13140/RG.2.2.19719.37289}, language = {de} } @article{AuerSuessDendorfer, author = {Auer, Simon and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Using markerless motion capture and musculoskeletal models: An evaluation of joint kinematics}, series = {Technology and Health Care}, journal = {Technology and Health Care}, publisher = {IOS Press}, issn = {0928-7329}, doi = {10.3233/THC-240202}, pages = {1 -- 10}, abstract = {BACKGROUND: This study presents a comprehensive comparison between a marker-based motion capture system (MMC) and a video-based motion capture system (VMC) in the context of kinematic analysis using musculoskeletal models. OBJECTIVE: Focusing on joint angles, the study aimed to evaluate the accuracy of VMC as a viable alternative for biomechanical research. METHODS: Eighteen healthy subjects performed isolated movements with 17 joint degrees of freedom, and their kinematic data were collected using both an MMC and a VMC setup. The kinematic data were entered into the AnyBody Modelling System, which enables the calculation of joint angles. The mean absolute error (MAE) was calculated to quantify the deviations between the two systems. RESULTS: The results showed good agreement between VMC and MMC at several joint angles. In particular, the shoulder, hip and knee joints showed small deviations in kinematics with MAE values of 4.8∘, 6.8∘ and 3.5∘, respectively. However, the study revealed problems in tracking hand and elbow movements, resulting in higher MAE values of 13.7∘ and 27.7∘. Deviations were also higher for head and thoracic movements. CONCLUSION: Overall, VMC showed promising results for lower body and shoulder kinematics. However, the tracking of the wrist and pelvis still needs to be refined. The research results provide a basis for further investigations that promote the fusion of VMC and musculoskeletal models.}, language = {en} } @article{FoerstlAdlerSuessetal., author = {F{\"o}rstl, Nikolas and Adler, Ina and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Technologies for Evaluation of Pelvic Floor Functionality: A Systematic Review}, series = {Sensors}, volume = {24}, journal = {Sensors}, number = {12}, publisher = {MDPI}, doi = {10.3390/s24124001}, abstract = {Pelvic floor dysfunction is a common problem in women and has a negative impact on their quality of life. The aim of this review was to provide a general overview of the current state of technology used to assess pelvic floor functionality. It also provides literature research of the physiological and anatomical factors that correlate with pelvic floor health. This systematic review was conducted according to the PRISMA guidelines. The PubMed, ScienceDirect, Cochrane Library, and IEEE databases were searched for publications on sensor technology for the assessment of pelvic floor functionality. Anatomical and physiological parameters were identified through a manual search. In the systematic review, 114 publications were included. Twelve different sensor technologies were identified. Information on the obtained parameters, sensor position, test activities, and subject characteristics was prepared in tabular form from each publication. A total of 16 anatomical and physiological parameters influencing pelvic floor health were identified in 17 published studies and ranked for their statistical significance. Taken together, this review could serve as a basis for the development of novel sensors which could allow for quantifiable prevention and diagnosis, as well as particularized documentation of rehabilitation processes related to pelvic floor dysfunctions.}, language = {en} } @misc{Dendorfer, author = {Dendorfer, Sebastian}, title = {Diagnostische Genauigkeit und klinische Anwendungen tragbarer Bewegungssensoren f{\"u}r die Kniegelenksrehabilitation}, address = {N{\"u}rnberg}, language = {de} }