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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.
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.
Musculoskeletal research questions regarding the prevention or rehabilitation of the hand can be addressed using inverse dynamics simulations when experiments are not possible. To date, no complete human hand model implemented in a holistic human body model has been fully developed. The aim of this work was to develop, implement, and validate a fully detailed hand model using the AnyBody Modelling System (AMS) (AnyBody, Aalborg, Denmark). To achieve this, a consistent multiple cadaver dataset, including all extrinsic and intrinsic muscles, served as a basis. Various obstacle methods were implemented to obtain with the correct alignment of the muscle paths together with the full range of motion of the fingers. These included tori, cylinders, and spherical ellipsoids. The origin points of the lumbrical muscles within the tendon of the flexor digitorum profundus added a unique feature to the model. Furthermore, the possibility of an entire patient-specific scaling based on the hand length and width were implemented in the model. For model validation, experimental datasets from the literature were used, which included the comparison of numerically calculated moment arms of the wrist, thumb, and index finger muscles. In general, the results displayed good comparability of the model and experimental data. However, the extrinsic muscles showed higher accordance than the intrinsic ones. Nevertheless, the results showed, that the proposed developed inverse dynamics hand model offers opportunities in a broad field of applications, where the muscles and joint forces of the forearm play a crucial role.
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.
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.
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.