@inproceedings{JungtaeublAurbachMelzneretal., author = {Jungt{\"a}ubl, Dominik and Aurbach, Maximilian and Melzner, Maximilian and Spicka, Jan and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {EMG-Based Validation of Musculoskeletal Models Considering Crosstalk}, series = {International Conference BIOMDLORE, June 28 - 30 2018, Białystok, Poland}, booktitle = {International Conference BIOMDLORE, June 28 - 30 2018, Białystok, Poland}, doi = {10.1109/BIOMDLORE.2018.8467211}, abstract = {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.}, language = {en} } @article{BarthelSuessDendorfer, author = {Barthel, Mareike and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Application of a transformer encoder for the prediction of intra-abdominal pressure}, series = {Computer Methods in Biomechanics and Biomedical Engineering}, journal = {Computer Methods in Biomechanics and Biomedical Engineering}, publisher = {Taylor \& Francis}, doi = {10.1080/10255842.2025.2586143}, pages = {11}, abstract = {Intra-abdominal pressure is a significant physiological parameter influencing spinal stability and pelvic floor health. This study investigates the potential of a transformer encoder model to predict IAP using motion capture data and musculoskeletal modeling. Data from 211 subjects performing walking, fast walking, and running were used to train a transformer encoder. The model showed promising results with an overall Mean Absolute Percentage Error of 13.5\% and a Pearson correlation coefficient of 0.85. Predictions for fast walking and running proved to be more challenging compared to walking, which was attributed to the greater variability and complexity of faster movements.}, language = {en} }