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 - CHAP A1 - Melzner, Maximilian A1 - Engelhardt, Lucas A1 - Havelkova, Leonard A1 - Simon, Ulrich A1 - Dendorfer, Sebastian T1 - A new musculoskeletal AnyBody detailed hand model validated by electromyography T2 - 16th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE) 2019, New York City, USA N2 - The AnyBody™ Modeling System (AMS) [1], is an universally used musculoskeletal simulation software using inverse dynamics. Until now, no complete human hand model is known in the AMS. Also considering other musculoskeletal software platforms, just one detailed entire hand model is recently published [2] but is only based on one subject. The aim of this work is to implement a full detailed hand model for the AMS including all extrinsic and intrinsic muscles using data by the UWB gained through an anatomical study of ten cadaver hands. Y1 - 2019 UR - https://www.researchgate.net/publication/336944957_A_NEW_MUSCULOSKELETAL_ANYBODY_DETAILED_HAND_MODEL_VALIDATED_BY_ELECTROMYOGRAPHY ER - TY - CHAP A1 - Suess, Franz A1 - Melzner, Maximilian A1 - Dendorfer, Sebastian T1 - Towards ergonomics working - machine learning algorithms and musculoskeletal modeling T2 - IOP Conference Series: Materials Science and Engineering N2 - Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments. Y1 - 2021 U6 - https://doi.org/10.1088/1757-899X/1208/1/012001 SN - 1757-899X N1 - Corresponding author: Sebastian Dendorfer VL - 1208 PB - IOP Publishing ER -