@misc{EnglertDendorfer, author = {Englert, Carsten and Dendorfer, Sebastian}, title = {Einfluss der Rotatorenmanschette auf die glenohumerale Stabilit{\"a}t}, series = {20. Intensivkurs Schulterendoprothetik Marburg}, journal = {20. Intensivkurs Schulterendoprothetik Marburg}, address = {Marburg}, language = {de} } @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} } @misc{SaffertMelznerDendorfer, author = {Saffert, Anne-Sophie and Melzner, Maximilian and Dendorfer, Sebastian}, title = {Biomechanical Analysis of the Right Elevated Glenohumeral Joint in Violinists during Legato-Playing}, series = {Biomdlore 2021: 21-23 October 2021 Vilnius/Trakai, Lithuania}, journal = {Biomdlore 2021: 21-23 October 2021 Vilnius/Trakai, Lithuania}, doi = {10.3233/THC-219001}, abstract = {BACKGROUND: Many statistics reveal that violin players suffer most often from musculoskeletal disorders compared to musicians of other instrument groups. A common phenomenon, especially observed in violin beginners, is the tendency to elevate the right shoulder during playing the violin. This can probably lead to serious disorders in long-term practice with repetitive movements. OBJECTIVE: For this reason, this study investigated the relationship between the right shoulder elevation and the force in the right glenohumeral joint during violin playing. It was hypothesized that the forces in the right glenohumeral joint are higher during playing with the right shoulder raised compared to playing in normal posture. METHODS: Motion capture data from four experienced violinists was recorded and processed by means of musculoskeletal simulation to get the force and elevation angle while playing with raised shoulder and in normal position. RESULTS: The results indicate that the absolute values of the resulting force, as well as the forces in the mediolateral, inferosuperior, and anteroposterior directions, are higher in playing the violin with the shoulder raised than in a normal posture. CONCLUSIONS: Elevating the right shoulder while playing the violin may pose a potential problem.}, language = {en} } @misc{PfingstenSchedelDendorferetal., author = {Pfingsten, Andrea and Schedel, Valentin and Dendorfer, Sebastian and Gschoßmann, Lukas}, title = {Biomechanical characteristics of knee rehabilitation exercises: a new approach for data-based exercise selection}, doi = {10.82161/v9a4-yt39}, pages = {8 min}, abstract = {Purpose: The principal objective of our research is to characterize key exercises that represent significant milestones in the rehabilitation process, employing biomechanical parameters to facilitate the distinction between stages of progression. Methods: We conducted an observational cross-sectional study on healthy volunteers (30 years), approved by an ethics committee. Subjects with a history of lower extremity disease were excluded. Movement data was collected, including the following hierarchy of exercise progressions: sit-to-stand-supported (STSS) - sit-to-stand (STS) - squat, stair climbing supported (SCS) - stair climbing (SC) - step-up, single-leg-stance-supported (SLSS) - single-leg-stance (SLS) - single-leg-stance-advanced (SLSA). Motion data was captured using a marker-less system (CapturyLive, v255, TheCaptury, Germany) and used for musculoskeletal simulations in the AnyBody Modeling System (v7.4.4, AnyBodyTechnology, Denmark) with a full-body model. ANOVA or Welch-Test with Bonferroni Post-Hoc was used to describe the anterior-posterior joint reaction force (JRF) normalized for body weight (BW) for each exercise. Positive values indicate an anterior JRF, and negative values a posterior JRF. Results: We recruited 31 subjects (female: 23/74.2\%) with a mean age of 22.81 years (SD:2.18). The analysis showed a significant difference in minimum JRF for STSS, STS, and squat (Welch-Test: F(2,56.421)=71.216, p0.001). Post-hoc analysis revealed that posterior JRF for STSS (mean:-2.63BW, SD:0.53) was lower than for STS (mean:-3.74BW, SD:0.42, p0.001), and STS was lower than squat (mean:-4.02BW, SD:0.35, p=0.043). No significant differences were observed in maximum JRF in the posterior direction (ANOVA: F(2,87)=1.151, p=0.324). For single-leg-stance exercises, the minimum JRF was similar for SLSS (mean:-0.34BW, SD:0.22) and SLS (mean:-0.55BW, SD:0.25, p=0.056), but higher in the posterior direction for SLSA (mean:-0.98BW, SD:0.49, p0.001; Welch-Test: F(2,54.379)=22.536, p0.001). Maximum JRF differences were also significant (ANOVA: F(2,87)=11.546, p0.001), with SLSS (mean:0.01BW, SD:0.22) showing a significant higher anterior JRF compared to SLS (mean:-0.16BW, SD:0.19, p=0.020). In the progression from SCS, SC to step-up, there was a significant difference in minimum JRF (ANOVA: F(2,87)=5.33, p=0.007), but not in maximum JRF (ANOVA: F(2,87)=0.624, p=0.538). SCS (mean:-3.42BW, SD:0.38) and SC (mean:-3.48BW, SD:0.54) showed no difference (p=1.000), but SC was significantly higher than step-up (mean:-3.58BW, SD:0.62, p=0.036). Conclusion(s): The exercises show differences in the JRF according to stages of progression, which can be taken into account when selecting exercises. Future research should address the group of patients who have undergone knee arthroplasty. Implications: In the future, it may be feasible to quantify patients' resilience based on sensor data and associate this with the optimal progression of an exercise sequence throughout the rehabilitation process.}, subject = {Physiotherapie}, language = {en} } @misc{Dendorfer, author = {Dendorfer, Sebastian}, title = {Towards a deeper understanding of Pelvic Floor Disorders via Biomechanical Models}, language = {en} }