@article{MelznerSuessDendorfer, author = {Melzner, Maximilian and Suess, Franz and Dendorfer, Sebastian}, title = {The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model - a sensitivity study}, series = {Computer Methods in Biomechanics and Biomedical Engineering}, volume = {25}, journal = {Computer Methods in Biomechanics and Biomedical Engineering}, number = {2}, publisher = {Taylor \& Francis}, issn = {1476-8259}, doi = {10.1080/10255842.2021.1940974}, pages = {156 -- 164}, abstract = {Outputs of musculoskeletal models should be considered probabilistic rather than deterministic as they are affected by inaccuracies and estimations associated with the development of the model. One of these uncertainties being critical for modeling arises from the determination of the muscles' line of action and the physiological cross-sectional area. Therefore, the aim of this study was to evaluate the outcome sensitivity of model predictions from a musculoskeletal hand model in comparison to the uncertainty of these input parameters. For this purpose, the kinematics and muscle activities of different hand movements (abduction of the fingers, abduction of the thumb, and flexion of the thumb) were recorded. One thousand simulations were calculated for each movement using the Latin hypercube sampling method with a corresponding variation of the muscle origin/insertion points and the cross-sectional area. Comparing the standard hand to simulations incorporating uncertainties of input parameters shows no major deviations in on- and off-set time point of muscle activities. About 60\% of simulations are located within a ± 30\% interval around the standard model concerning joint reaction forces. The comparison with the variation of the input data leads to the conclusion that the standard hand model is able to provide not over-scattered outcomes and, therefore, can be considered relatively stable. These results are of practical importance to the personalization of a musculoskeletal model with subject-specific bone geometries and hence changed muscle line of action.}, subject = {Biomechanik}, language = {en} } @article{MelznerEngelhardtSimonetal., author = {Melzner, Maximilian and Engelhardt, Lucas and Simon, Ulrich and Dendorfer, Sebastian}, title = {Electromyography-Based Validation of a Musculoskeletal Hand Model}, series = {Journal of Biomechanical Engineering}, volume = {144}, journal = {Journal of Biomechanical Engineering}, number = {2}, publisher = {American Society of Mechanical Engineers, ASME}, doi = {10.1115/1.4052115}, pages = {8}, abstract = {Regarding the prevention of injuries and rehabilitation of the human hand, musculoskeletal simulations using an inverse dynamics approach allow for insights of the muscle recruitment and thus acting forces on the hand. Currently, several hand models from various research groups are in use, which are mainly validated by the comparison of numerical and anatomical moment arms. In contrast to this validation and model-building technique by cadaver studies, the aim of this study is to further validate a recently published hand model [1] by analyzing numerically calculated muscle activities in comparison to experimentally measured electromyographical signals of the muscles. Therefore, the electromyographical signals of 10 hand muscles of five test subjects performing seven different hand movements were measured. The kinematics of these tasks were used as input for the hand model, and the numerical muscle activities were computed. To analyze the relationship between simulated and measured activities, the time difference of the muscle on- and off-set points was calculated, which resulted in a mean on- and off-set time difference of 0.58 s between the experimental data and the model. The largest differences were detected for movements that mainly addressed the wrist. One major issue comparing simulated and measured muscle activities of the hand is cross-talk. Nevertheless, the results show that the hand model fits the experiment quite accurately despite some limitations and is a further step toward patient-specific modeling of the upper extremity.}, subject = {Elektromyographie}, language = {en} } @inproceedings{SuessMelznerDendorfer, author = {Suess, Franz and Melzner, Maximilian and Dendorfer, Sebastian}, title = {Towards ergonomics working - machine learning algorithms and musculoskeletal modeling}, series = {IOP Conference Series: Materials Science and Engineering}, volume = {1208}, booktitle = {IOP Conference Series: Materials Science and Engineering}, publisher = {IOP Publishing}, issn = {1757-899X}, doi = {10.1088/1757-899X/1208/1/012001}, abstract = {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.}, language = {en} }