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Static pelvic tilt impacts functional cup position in total hip arthroplasty (THA). In the current study we investigated the effect of kinematic pelvic changes on cup position. In the course of a prospective controlled trial postoperative 3D-computed tomography (CT) and gait analysis before and 6 and 12 months after THA were obtained in 60 patients. Kinematic pelvic motion during gait was measured using Anybody Modeling System. By fusion with 3D-CT, the impact of kinematic pelvic tilt alterations on cup anteversion and inclination was calculated. Furthermore, risk factors correlating with high pelvic mobility were evaluated. During gait a high pelvic range of motion up to 15.6° exceeding 5° in 61.7% (37/60) of patients before THA was found. After surgery, the pelvis tilted posteriorly by a mean of 4.0 ± 6.6° (p < .001). The pelvic anteflexion led to a mean decrease of −1.9 ± 2.2° (p < .001) for cup inclination and −15.1 ± 6.1° (p < .001) for anteversion in relation to the anterior pelvic plane (APP). Kinematic pelvic changes resulted in a further change up to 2.3° for inclination and up to 12.3° for anteversion. In relation to the preoperative situation differences in postoperative cup position ranged from −4.4 to 4.6° for inclination and from −7.8 to 17.9° for anteversion, respectively. Female sex (p < .001) and normal body weight (p < .001) correlated with high alterations in pelvic tilt. Kinematic pelvic changes highly impact cup anteversion in THA. Surgeons using the APP as reference should aim for a higher anteversion of about 15° due to the functional anteflexion of the pelvis during gait.
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.
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.