TY - CHAP A1 - Suess, Franz A1 - Melzner, Maximilian A1 - Dendorfer, Sebastian T1 - Towards ergonomics working - machine learning algorithms and musculoskeletal modeling T2 - IOP Conference Series: Materials Science and Engineering N2 - Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments. Y1 - 2021 U6 - https://doi.org/10.1088/1757-899X/1208/1/012001 SN - 1757-899X N1 - Corresponding author: Sebastian Dendorfer VL - 1208 PB - IOP Publishing ER - TY - CHAP A1 - Aurbach, Maximilian A1 - Jungtäubl, Dominik A1 - Spicka, Jan A1 - Dendorfer, Sebastian T1 - EMG-based validation of musculoskeletal models considering crosstalk T2 - World Congress Biomechanics, 28-30 June 2018, Dublin N2 - BACKGROUND: Validation and verification of multibody musculoskeletal models sEMG is a difficult process because of the reliability of sEMG data and the complex relationship of muscle force and sEMG. OBJECTIVE: This work aims at comparing experimentally recorded and simulated muscle activities considering a numerical model for crosstalk. METHODS: For providing an experimentally derived reference data set, subjects were performing elevations of the arm, where the activities of the contemplated muscle groups were measured by sEMG sensors. Computed muscle activities were further processed and transformed into an artificial electromyographical signal, which includes a numerical crosstalk model. In order to determine whether the crosstalk model provides a better agreement with the measured muscle activities, the Pearson correlation coefficient has been computed as a qualitative way of assessing the curve progression of the data sets. RESULTS: The results show an improvement in the correlation coefficient between the experimental data and the simulated muscle activities when taking crosstalk into account. CONCLUSIONS: Although the correlation coefficient increased when the crosstalk model was utilized, it is questionable if the discretization of both, the crosstalk and the musculoskeletal model, is accurate enough. Y1 - 2018 U6 - https://doi.org/10.1109/BIOMDLORE.2018.8467211 ER - TY - GEN A1 - Suess, Franz A1 - Melzner, Maximilian A1 - Dendorfer, Sebastian T1 - Towards Ergonomic working - machine learning algorithms and musculoskeletal modeling T2 - RIM 2021, 13th International Scientific Conference on Manufacturing Engineering, 29 Sept. - 1 Oct 2021, Sarajevo, Bosnia and Herzegovina Y1 - 2021 ER - TY - RPRT A1 - Putzer, Michael A1 - Rasmussen, John A1 - Ehrlich, Ingo A1 - Gebbeken, Norbert A1 - Dendorfer, Sebastian ED - Baier, Wolfgang T1 - Muskuloskelettale Simulation zur Untersuchung des Einflusses geometrischer Parameter der Wirbelkörper auf die Belastung der Lendenwirbelsäule T2 - Forschungsbericht 2013 / Ostbayerische Technische Hochschule Regensburg Y1 - 2013 UR - https://doi.org/10.35096/othr/pub-799 SP - 60 EP - 61 CY - Regensburg ER - TY - GEN A1 - Dendorfer, Sebastian T1 - Sturz- und Frakturprävention: Use it or lose it! T2 - 10 Jahre Alterstraumatologie Caritas Krankenhaus St. Josef Regensburg Y1 - 2024 ER - TY - CHAP A1 - Dendorfer, Sebastian A1 - Englert, Carsten T1 - Forces on a clavicles midshaft fracture and influence of fracture type T2 - AO Symposium, Regensburg, 2009 Y1 - 2009 ER - TY - JOUR A1 - Förstl, Nikolas A1 - Süß, Franz A1 - Englert, Carsten A1 - Dendorfer, Sebastian T1 - Design of a reverse shoulder implant to measure shoulder stiffness during implant component positioning JF - Medical Engineering & Physics N2 - To avoid dislocation of the shoulder joint after reverse total shoulder arthroplasty, it is important to achieve sufficient shoulder stability when placing the implant components during surgery. One parameter for assessing shoulder stability can be shoulder stiffness. The aim of this research was to develop a temporary reverse shoulder implant prototype that would allow intraoperative measurement of shoulder stiffness while varying the position of the implant components. Joint angle and torque measurement techniques were developed to determine shoulder stiffness. Hall sensors were used to measure the joint angles by converting the magnetic flux densities into angles. The accuracy of the joint angle measurements was tested using a test bench. Torques were determined by using thin-film pressure sensors. Various mechanical mechanisms for variable positioning of the implant components were integrated into the prototype. The results of the joint angle measurements showed measurement errors of less than 5° in a deflection range of ±15° adduction/abduction combined with ±45° flexion/extension. The proposed design provides a first approach for intra-operative assessment of shoulder stiffness. The findings can be used as a technological basis for further developments. Y1 - 2023 U6 - https://doi.org/10.1016/j.medengphy.2023.104059 N1 - Corresponding author: Sebstian Dendorfer VL - 121 PB - Elsevier ET - Journal Pre-proof ER - TY - CHAP A1 - Muehling, M. A1 - Englert, Carsten A1 - Dendorfer, Sebastian T1 - Influence of biceps tenotomy and tenodesis on post-operative shoulder strength T2 - Jahrestagung der Deutschen Gesellschaft für Biomechanik, March 2017, Hannover, Germany Y1 - 2017 ER - TY - CHAP A1 - Englert, Carsten A1 - Müller, F. A1 - Dendorfer, Sebastian T1 - Einfluss der Muskelkräfte, des Bewegungsausmaßes und der Bruchform auf die Kraftübertragung des Implantat-Knochenverbundes am Beispiel der Claviculafraktur im mittleren Drittel T2 - 17. Jahreskongress der Deutschen Vereinigung für Schulter- und Ellenbogenchirurgie (DVSE), Rosenheim 2010 N2 - Fragestellung Es soll in dieser Computersimulationsstudie untersucht werden, wie der Osteosyntheseverbund Platte mit Schrauben im Verbund mit einer im mittleren Drittel gebrochenen Clavicula durch das Bewegungsausmaß in vivo belastet ist. Was sind die grundlegenden Kräfte die auf Clavicula und Implantat wirken und welchen Einfl uss hat die Bruchform. Methodik Die Muskel- und Gelenkkräfte sowie die Belastung des Implantatverbundes wurden mit einer muskuloskelletalen Simulationssoftware (AnyBody Technology, V.4) berechnet. Hierfür wurden mit einem komplexen Model des menschlichen Körpers folgende Bewegungen analysiert: eine Flexion von 160° und Abduktion 160° mit einem Gewicht von 2 kg in der Hand. Aus CT-Patientendaten wurden zwei dreidimensionale Modelle des Clavicula-Implantat Verbundes gebildet, die sich in der Frakturform unterscheiden (Querfraktur und vertikale Fraktur). In beiden Modellen wurde eine Claviculaosteosynthese in superiorer Position mit einer 6 Loch LCP mit 2 Schrauben pro Hauptfragment verwendet. Die Materialeigenschaften wurden aus der Dichte des Materials sowie aus Literaturdaten verwendet. Die Muskel- und Gelenkkräfte aus der muskuloskelletalen Berechnung wurden auf das Finite Elemente Modell übertragen und die Spannungen und Dehnungen des Implantat-Knochenverbundes wurden berechnet. Ergebnisse Es zeigte sich, dass die simulierte in vivo Belastung stark abhängig vom Flexionswinkel ist. Das Implantat ist in der superioren Lage auf Biegung belastet, welche maximale Werte im Überschulterniveau erreicht. Die Bruchform mit anatomischer Reposition und Kontakt der Hauptfragmente zueinander führt zu einer deutlichen Entlastung des Osteosyntheseverbundes im Vergleich zu einer Bruchform mit vertikaler Fraktur. Schlussfolgerung Aus den Analysen ist eine Positionierung der Plattenosteosynthese für die im mittleren Drittel frakturierte Clavicula in anterior-superiorer Lage wünschenswert. Die anatomische Reposition entlastet den Osteosyntheseverbund und sollte möglichst erreicht werden. Die Nachbehandlung sollte ein Bewegungsausmaß für den Arm für 4 Wochen für einfache Bruchformen auf 70° Flexion und Abduktion limitieren und für komplexe Bruchformen diese Limitierung ausgedehnt werden. Y1 - 2010 ER - TY - JOUR A1 - Pfeifer, Christian A1 - Müller, Michael A1 - Prantl, Lukas A1 - Berner, Arne A1 - Dendorfer, Sebastian A1 - Englert, Carsten T1 - Cartilage labelling for mechanical testing in T-peel configuration JF - International Orthopaedics N2 - PURPOSE: The purpose of this study was to find a suitable method of labelling cartilage samples for the measurement of distraction distances in biomechanical testing. METHODS: Samples of bovine cartilage were labelled using five different methods: hydroquinone and silver nitrate (AgNO3), potassium permanganate (KMnO4) with sodium thiosulphate (Na2S2O3), India ink, heat, and laser energy. After the labelling, we analysed the cartilage samples with regard to cytotoxity by histochemical staining with ethidiumbromide homodimer (EthD-1) and calcein AM. Furthermore, we tested cartilages labelled with India ink and heat in a T-peel test configuration to analyse possible changes in the mechanical behaviour between marked and unlabelled samples. RESULTS: Only the labelling methods with Indian ink or a heated needle showed acceptable results in the cytotoxity test with regard to labelling persistence, accuracy, and the influence on consistency and viability of the chondrocytes. In the biomechanical T-peel configuration, heat-labelled samples collapsed significantly earlier than unlabelled samples. CONCLUSION: Labelling bovine cartilage samples with Indian ink in biomechanical testing is a reliable, accurate, inexpensive, and easy-to-perform method. This labelling method influenced neither the biomechanical behaviour nor the viability of the tissue compared to untreated bovine cartilage. KW - Bovine cartilage KW - Cartilage samples KW - Indian ink KW - T-peel configuration KW - Method labeling KW - Knorpel KW - Rind KW - Kennzeichnung KW - Tinte KW - Biomechanik KW - Prüfung Y1 - 2012 U6 - https://doi.org/10.1007/s00264-011-1468-3 VL - 36 IS - 7 SP - 1493 EP - 1499 PB - Springer ER - TY - JOUR A1 - Förstl, Nikolas A1 - Adler, Ina A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - Technologies for Evaluation of Pelvic Floor Functionality: A Systematic Review JF - Sensors N2 - 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. Y1 - 2024 U6 - https://doi.org/10.3390/s24124001 N1 - Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7306 VL - 24 IS - 12 PB - MDPI ER - TY - GEN A1 - Englert, Carsten A1 - Dendorfer, Sebastian T1 - Einfluss der Rotatorenmanschette auf die glenohumerale Stabilität T2 - 20. Intensivkurs Schulterendoprothetik Marburg KW - Biomechanik Y1 - 2022 CY - Marburg ER - TY - GEN A1 - Förstl, Nikolas A1 - Adler, Ina A1 - Suess, Franz A1 - Čechová, Hana A1 - Jansová, Magdalena A1 - Cimrman, Robert A1 - Vychytil, Jan A1 - Dendorfer, Sebastian T1 - Workflow for the development of a non-invasive feedback device to assess pelvic floor contractions T2 - ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland N2 - Introduction Pelvic floor disorders affect about 40% of women worldwide [1]. Pelvic floor muscle (PFM) training is both a preventive and a therapeutic intervention. Current PFM training devices are invasive and have little scientific evidence. The idea is to develop a noninvasive feedback device to assess adequate PFM contraction. Therefore, evidence-based female musculoskeletal models, non-invasive data acquisition, sensor technology and artificial intelligence (AI) will be combined. This work presents the workflow to achieve such a feedback device and describes the interaction of the technologies used. Methods Exercises that induce PFM contractions have been evaluated and defined. Motion capture of these exercises will provide input for female musculoskeletal models. A combination of biomechanical rigid body and FEM simulations will be used to estimate PFM contractions. In addition, a non-invasive sensor will measure pelvic floor activity. The simulated and measured data will be used to develop an AI model that provides feedback on PFM contractions based on non-invasive data collection. Results The AMMR (AnyBody Managed Model Repository) of the AnyBody modelling system (AMS, Aalborg, Denmark) serves as the initial model for performing inverse dynamic simulations of the exercises. To calculate the PFM forces, the full-body model must be supplemented with the relevant pelvic floor structures and a mass model of the internal organs. A modified abdominal pressure model must also be incorporated. The AMS calculates the PFM activities caused by the internal organ loads and the generated abdominal pressure during the exercises. The muscle activities are transferred to a FEM model of the female pelvic floor (SfePy, simple finite elements in Python). The identical pelvic floor structures were integrated into the FEM model as in the AMS. Active PFM contractions can be simulated using the FEM model. Movement of the coccyx due to PFM contractions has been reported in the literature [2,3]. Therefore, a noninvasive coccyx motion sensor will be developed to provide additional information on PFM contractions. The measured data (coccyx motion sensor, motion capture) and the simulation results of the models will be combined to create an AI feedback model using Python. The final feedback device will consist of the AI model and the developed coccyx motion sensor, which can reproduce the resulting PFM contractions based on the sensor data and simplified motion tracking. Discussion The creation of the AMS and the FEM model is a prerequisite for the development of the feedback device. The relevant structures in the models are located inside the body. This limits the ability to observe the structures during the exercises, which can lead to difficulties in model validation. The development of a user-friendly sensor with sufficient measuring accuracy of the coccyx motion is another challenge. Nevertheless, the workflow represents a promising approach to develop a noninvasive feedback system to assess PFM contraction. References 1. Wang et al, Front Public Health, 10:975829, 2022. 2. Bø et al, Neurourol Urodyn, 20:167–174, 2001. 3. Fujisaki et al, J Phys Ther Sci, 30:544–548, 2018. Acknowledgements This work was supported by the project no. BYCZ01-014 of the Program INTERREG Bavaria – Czechia 2021–2027. Y1 - 2024 ER -