BackgroundThere is a wide range of mechanical properties of spinal ligaments documented in literature. Due to the fact that ligaments contribute in stabilizing the spine by limiting excessive intersegmental motion, those properties are of particular interest for the implementation in musculoskeletal models. The aim of this study was to investigate the effect of varying ligament stiffness on the kinematic behaviour of the lumbar spine.MethodsA musculoskeletal model with a detailed lumbar spine was modified according to fluoroscopic recordings and corresponding data files of three different subjects. For flexion, inverse dynamics analysis with a variation of the ligament stiffness matrix were conducted. The influence of several degrees of ligament stiffness on the lumbar spine model were investigated by tracking ligament forces, disc forces and resulting moments generated by the ligaments. Additionally, the kinematics of the motion segments were evaluated.ResultsAn increase of ligament stiffness resulted in an increase of ligament and disc forces, whereas the relative change of disc force increased at a higher rate at the L4/L5 level (19 %) than at the L3/L4 (10 %) level in a fully flexed posture. The same behaviour applied to measured moments with 67 % and 45 %. As a consequence, the motion deflected to the lower levels of the lumbar spine and the lower discs had to resist an increase in loading.ConclusionsHigher values of ligament stiffness over all lumbar levels could lead to a shift of the loading and the motion between segments to the lower lumbar levels. This could lead to an increased risk for the lower lumbar parts.

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.

Imbalanced spines have a risk of increased compression forces at Th12-L1. L4-L5 always has increased spinal loads. These slides can be retrieved under Electronic Supplementary Material.

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.