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Musculoskeletal models of the shoulder complex are valuable research aids to investigate tears of the supraspinatus and the resulting mechanical impact during abduction of the humerus. One of the major contributors to this motion is the deltoid muscle group and for this, an accurate modeling of the lines of action is indispensable. The aim of this work was to utilize a torus obstacle wrapping approach for the deltoids of an existing shoulder model and assess the feasibility of the approach during humeral abduction. The shoulder model from the AnyBody™ modeling system was used as a platform. The size of the tori is based on a magnetic resonance imaging (MRI) approach and several kinematic couplings are implemented to determine the trajectories of the tori during abduction. To assess the model behavior, the moment arms of the virtual muscle elements and the resultant glenohumeral joint reaction force (GHJF) were compared with reference data from the literature during abduction of the humerus in the range 20°–120°. The root mean square error for the anterior, lateral and posterior part between the simulated muscle elements and reference data from the literature was 3.9, 1.7 and 5.8 mm, respectively. The largest deviation occurred on the outer elements of the muscle groups, with 12.6, 10.4 and 20.5 mm, respectively. During abduction, there is no overlapping of the muscle elements and these are in continuous contact with the torus obstacles, thus enabling a continuous force transmission. This results in a rising trend of the resultant GHJF.
The torus obstacle approach as a wrapping method for the deltoid muscles provides a guided muscle pathing by simultaneously approximating the curvature of the deltoid muscle. The results from the comparison of the simulated moment arms and the resultant GHJF are in accordance with those in the literature in the range 20°–120° of abduction.
Although this study shows the strength of the torus obstacle as a wrapping approach, the method of fitting the tori according to MRI data was not suitable. A cadaver study is recommended to better validate and mathematically describe the torus approach.
Based on electromyographic data and force measurements within the shoulder joint, there is an indication that muscle and resulting joint reaction forces keep increasing over an abduction angle of 90°. In inverse dynamics models, no single parameter could be attributed to simulate this force behaviour accordingly. The aim of this work is to implement kinematic, kinetic and muscle model modifications to an existing model of the shoulder (AnyBody™) and assess their single and combined effects during abduction up to 140° humeral elevation. The kinematics and the EMG activity of 10 test subjects were measured during humeral abduction. Six modifications were implemented in the model: alternative wrapping of the virtual deltoid muscle elements, utilization of a three element Hill model, strength scaling, motion capture driven clavicle elevation/protraction, translation of the GH joint in dependency of the acting forces and an alteration of the scapula/clavicle rhythm. From the six modifications, 16 different combinations were considered. Parameter combinations with the Hill model changed the resultant GH joint reaction force and led to an increase in force during abduction of the humerus above 90°. Under the premise of muscle activities and forces within the GH joint rising after 90° of humeral abduction, we propose that the Hill type muscle model is a crucial parameter for accurately modelling the shoulder. Furthermore, the outcome of this study indicates that the Hill model induces the co-contraction of the muscles of the shoulder without the need of an additional stability criterion for an inverse dynamics approach.
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.
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.