TY - GEN A1 - Melzner, Maximilian A1 - Pfeifer, Christian A1 - Alt, V. A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - Änderung der Gelenkreaktionskraft bei Schädigung des medialen Bandapparates im Ellenbogen T2 - Zeitschrift fur Orthopadie und Unfallchirurgie KW - Muskuloskelettale Simulation KW - Ellenbogenstabilität Y1 - 2020 U6 - https://doi.org/10.1055/s-0040-1717270 N1 - Poster VL - 158 IS - S01 PB - Thieme ER - TY - JOUR A1 - Dendorfer, Sebastian T1 - Älterwerden muss auch mal wehtun! JF - Gesunde Hochschule, OTH Regensburg, 4.7.2016 Y1 - 2016 ER - TY - GEN A1 - Dendorfer, Sebastian T1 - Zahlen statt Meinung – virtuelle Ganzkörpermodelle für die Mensch-Fahrzeug-Interaktion Y1 - 2025 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 - TY - VIDEO A1 - Auer, Simon A1 - Reinker, Lukas A1 - Süß, Franz A1 - Kubowitsch, Simone A1 - Krutsch, Werner A1 - Weber, Markus A1 - Renkawitz, Tobias A1 - Dendorfer, Sebastian T1 - Webcast: Effect of mental demand on leg loading in highly dynamic motion N2 - Football players have a high risk of leg muscle injuries, especially when exposed to mental stress. Injuries to muscles of the thigh are common in amateur and professional football, representing almost a third of all injuries. These injuries occur primarily in non-contact situations and from overuse. They can lead to a range of costs, including financial costs associated with treatment as well as those associated with long-term recovery, and absence from training and/or competition. Further, there is a high risk of injury recurrence and subsequent injury. KW - Webcast KW - AnyBody KW - Stress KW - Football KW - Speedcourt Y1 - 2020 UR - https://www.youtube.com/watch?v=uSc_9XlnkaA ER - TY - GEN A1 - Dendorfer, Sebastian T1 - Virtuelle Modelle der oberen Extremität T2 - 22. Interdisziplinäres Symposium Medizin-Physiotherapie-Sportwissenschaften, 22.-23.11.2019, Regensburg, Germany Y1 - 2019 ER - TY - CHAP A1 - Dendorfer, Sebastian T1 - Virtuelle Menschmodelle – von der Bewegung zur Belastung T2 - Medbo Bezirksklinikum Regensburg, April 2016 Y1 - 2016 ER - TY - CHAP A1 - Weber, Tim A1 - Dendorfer, Sebastian A1 - Grifka, Joachim A1 - Weber, Markus A1 - Wörner, Michael A1 - Dullien, Silvia A1 - Verkerke, Gijsbertus Jacob A1 - Renkawitz, Tobias T1 - Verbessert die computerassistierte Femur First Operationstechnik für die Hüftendoprothetik den muskuloskelettalen Lastfall auf das Hüftgelenk? T2 - DKOU 2015, Deutscher Kongress für Orthopädie und Unfallchirurgie 2015 Y1 - 2015 UR - https://www.researchgate.net/publication/283259007_Verbessert_die_computerassistierte_Femur_First_Operationstechnik_fur_die_Huftendoprothetik_den_muskuloskelettalen_Lastfall_auf_das_Huftgelenk ER - TY - JOUR A1 - Weber, Tim A1 - Dullien, Silvia A1 - Grifka, Joachim A1 - Renkawitz, Tobias A1 - Dendorfer, Sebastian T1 - Validation of a Motion Capture Laboratory and a new marker-placement protcol for clinical applications JF - Gait & Posture Y1 - 2013 U6 - https://doi.org/10.1016/j.gaitpost.2013.07.229 VL - 38 IS - Suppl. 1 SP - 113 EP - 114 ER - TY - CHAP A1 - Andersen, Michael Skipper A1 - de Zee, Mark A1 - Dendorfer, Sebastian A1 - MacWilliams, Bruce A1 - Rasmussen, John T1 - Validation of a detailed lower extremity model based on the Klein Horsman data set T2 - Proceedings of the 12th International Symposium on Computer Simulation in Biomechanics (ISB 2009), July 2nd - 4th 2009, Cape Town, South Africa Y1 - 2009 SP - 27 EP - 28 ER -