@article{RusavyCechovaDendorferetal., author = {Rusavy, Zdenek and Cechova, Hana and Dendorfer, Sebastian and Kalis, Vladimir and Ismail, Khaled M.}, title = {Is the Finnish grip tight enough? A manometric study of two manual perineal protection techniques}, series = {Acta Obstetricia et Gynecologica Scandinavica}, journal = {Acta Obstetricia et Gynecologica Scandinavica}, edition = {Early view}, publisher = {Wiley}, doi = {10.1111/aogs.15033}, language = {en} } @misc{FoerstlAdlerSuessetal., author = {F{\"o}rstl, Nikolas and Adler, Ina and Suess, Franz and Čechov{\´a}, Hana and Jansov{\´a}, Magdalena and Cimrman, Robert and Vychytil, Jan and Dendorfer, Sebastian}, title = {Workflow for the development of a non-invasive feedback device to assess pelvic floor contractions}, series = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, journal = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, abstract = {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. B{\o} 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.}, language = {en} } @article{RusavyCechovaDendorferetal., author = {Rusavy, Zdenek and Cechova, Hana and Dendorfer, Sebastian and Kalis, Vladimir and Ismail, Khaled M.}, title = {Is the Finnish grip tight enough? A manometric study of two manual perineal protection techniques}, series = {Acta Obstetricia et Gynecologica Scandinavica}, volume = {104}, journal = {Acta Obstetricia et Gynecologica Scandinavica}, number = {2}, publisher = {Wiley}, doi = {10.1111/aogs.15033}, pages = {423 -- 425}, language = {en} } @misc{AdlerFoerstlČechovaetal., author = {Adler, Ina and F{\"o}rstl, Nikolas and Čechov{\´a}, Hana and Nov{\´a}ček, V{\´i}t and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Mechanistic analysis of pelvic floor functionality using musculoskeletal simulation}, series = {ISB 2025 - The XXX Congress of the International Society of Biomechanics, 27-31 July 2025, Stockholm}, journal = {ISB 2025 - The XXX Congress of the International Society of Biomechanics, 27-31 July 2025, Stockholm}, doi = {10.35096/othr/pub-8729}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-87294}, pages = {1}, abstract = {There is consensus that knowledge about the fundamentals of the pelvic floor remains incomplete, particularly regarding the sensitivity of individual pelvic floor muscles to posture variations. This study aimed to investigate the effects of variations in pelvic tilt and the point of force application induced by changes in posture on pelvic floor activity using musculoskeletal simulation. A parameter study analysed various loading cases, highlighting the coherent response of individual pelvic floor muscles to loads. Muscle activities and forces were compared across different force application points and pelvic tilt angles. A key finding was identifying peak muscle activity conditions that could help better understand the causes of pelvic floor disorders.}, language = {en} }