Open Access

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.

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.

The gold standard for the analysis of human kinematics and kinetics is a camera-based motion capture system in combination with force measurement platforms. Alternatively, inertial measurement units can be utilized to obtain human kinematics, while ground reaction forces are computed from full body dynamics. This setup represents a system independent from the spatial confinement of a gait laboratory. The aim of this study is the comparison of the two methods by the investigation of lower limb kinematics and the resulting joint reaction forces within the ankle-, knee- and hip joints. For this purpose, human motion during gait was captured simultaneously by both measurement techniques. 13 trials from 8 different test subjects were evaluated in total. IMU data was processed with a quaternion based Kalman Filter. The data sets were implemented into a musculoskeletal simulation program in order to drive a virtual human body model. Each sensor was aligned to the gravitational and magnetic field vectors of the earth. The angles of flexions, extensions and rotations were analyzed to determine kinematic differences. Joint reaction forces defined kinetic dissimilarities. The overall kinematic differences of both models yielded root mean square errors of 7.62°, 6.02°, 4.95°, 2.79°, 2.38° and 3.56° for ankle flexion, subtalar eversion, knee flexion, hip external rotation, hip abduction and hip flexion, respectively. The proximo-distal differences in force peaks between the models yielded overall for the ankle, 57.33 %Bodyweight(BW) ± 46.86 %BW (16.66 %(Maximum peak to peak) ± 13.62 %) for the knee 37.09 %BW ± 29.33 %BW (17.65 % ± 15.44 %) and 32.03 %BW ± 24.33 %BW (15.6 % ± 12.54 %) for the hip. The overall outcome of this work investigated an approach independent of the common setup of the gait laboratory, thus enabling a cheaper and more flexible technology as an alternative. However, kinematic and thus kinetic differences remain rather large. Future work aims to improve the contact criterion for the calculation of the ground reaction forces and the implementation of a full-body calibration algorithm for the IMU system in order to counteract magnetic field disturbances.