TY - CHAP A1 - Jungtäubl, Dominik A1 - Aurbach, Maximilian A1 - Melzner, Maximilian A1 - Spicka, Jan A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - EMG-Based Validation of Musculoskeletal Models Considering Crosstalk T2 - International Conference BIOMDLORE, June 28 - 30 2018, Białystok, Poland 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. KW - musculoskeletal modeling KW - validation KW - surface electromyography KW - crosstalk Y1 - 2018 U6 - https://doi.org/10.1109/BIOMDLORE.2018.8467211 ER - TY - JOUR A1 - Barthel, Mareike A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - Application of a transformer encoder for the prediction of intra-abdominal pressure JF - Computer Methods in Biomechanics and Biomedical Engineering N2 - 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. KW - Intra-abdominal pressure KW - transformer encoder KW - machine learning KW - motion capture KW - musculoskeletal modeling Y1 - 2025 U6 - https://doi.org/10.1080/10255842.2025.2586143 N1 - Corresponding author der OTH Regensburg: Mareike Barthel PB - Taylor & Francis ER -