Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.

Marker location and knee joint constraint affect the reporting of overhead squat kinematics in elite youth football players

Motion capture systems are used in the analysis and interpretation of athlete movement patterns for a variety of reasons, but data integrity remains critical regardless. The extent to which marker location or constraining degrees of freedom (DOF) in the biomechanical model impacts on this integrity lacks consensus. Ten elite academy footballers performed bilateral overhead squats using a marker-based motion capture system. Kinematic data were calculated using four different marker sets with 3DOF and 6DOF configurations for the three joint rotations of the right knee. Root mean squared error differences between marker sets ranged in the sagittal plane between 1.02 and 4.19 degrees to larger values in the frontal (1.30-6.39 degrees) and transverse planes (1.33 and 7.97 degrees). The cross-correlation function of the knee kinematic time series for all eight marker-sets ranged from excellent for sagittal plane motion (>0.99) but reduced for both coronal and transverse planes (<0.9). Two-way ANOVA repeated measures calculated at peak knee flexion revealed significant differences between marker sets for frontal and transverse planes (p < 0.05). Pairwise comparisons showed significant differences between some marker sets. Marker location and constraining DOF while measuring relatively large ranges of motion in this population are important considerations for data integrity.

Female Lower Body Muscle Forces: A Musculoskeletal Modeling Comparison of Back Squats, Split Squats and Good Mornings

The aim of this study was to analyze lower leg muscle forces during strength exercises such as back squats, good mornings and split squats, with a particular emphasis on females. By focusing on females, who are more vulnerable to anterior cruciate ligament injuries, we aimed to better understand muscle engagement and its role in injury prevention. Eight participants were monitored during exercises with a barbell load of 25% of body weight and, during the back squat, an additional 50% load. The analysis was conducted using personalized musculoskeletal models, electromyography (EMG) and Vicon motion capture systems to assess various muscle groups, including the m. gluteus maximus and m. gluteus medius, as well as the hamstring and quadriceps muscles. The back squat produced the highest forces for the quadriceps muscles, particularly the rectus femoris (>25 N/kg), as well as in the back leg during the split squat (>15 N/kg). The gluteal muscles were most active during good mornings and in the front leg of the split squat, especially the m. gluteus maximus medial part (>20 N/kg). The hamstrings generated the highest muscle forces in the front leg of the split squat, with the greatest forces observed in the m. semimembranosus. Our research highlights how musculoskeletal modeling helps us to understand the relationship among muscles, joint angles and anterior cruciate ligament injury risks, especially in strength training females. The results emphasize the need for personalized exercise guidance and customized models to make strength training safer and more effective.

Changes in segment coordination variability and the impacts of the lower limb across running mileages in half marathons: Implications for running injuries

BACKGROUND

Segment coordination variability (CV) is a movement pattern associated with running-related injuries. It can also be adversely affected by a prolonged run. However, research on this topic is currently limited. The purpose of this study was to investigate the effects of a prolonged run on segment CV and vertical loading rates during a treadmill half marathon.

METHODS

Fifteen healthy runners ran a half marathon on an instrumental treadmill in a biomechanical laboratory. Synchronized kinematic and kinetic data were collected every 2 km (from 2 km until 20 km), and the data were processed by musculoskeletal modeling. Segment CVs were computed from the angle-angle plots of selected pelvis-thigh, thigh-shank, and shank-rearfoot couplings using a modified vector coding technique. The loading rate of vertical ground reaction force was also calculated. A one-way MANOVA with repeated measures was performed on each of the outcome variables to examine the main effect of running mileage.

RESULTS

Significant effects of running mileage were found on segment CVs (p ≤ 0.010) but not on loading rate (p = 0.881). Notably, during the early stance phase, the CV of pelvis frontal vs. thigh frontal was significantly increased at 20 km compared with the CV at 8 km (g = 0.59, p = 0.022). The CV of shank transverse vs. rearfoot frontal decreased from 2 km to 8 km (g = 0.30, p = 0.020) but then significantly increased at both 18 km (g = 0.05, p < 0.001) and 20 km (g = 0.36, p < 0.001).

CONCLUSION

At the early stance, runners maintained stable CVs on the sagittal plane, which could explain the unchanged loading rate throughout the half marathon. However, increased CVs on the frontal/transverse plane may be an early sign of fatigue and indicative of possible injury risk. Further studies are necessary for conclusive statements in this regard.

An Anatomical-Based Subject-Specific Model of In-Vivo Knee Joint 3D Kinematics From Medical Imaging

Biomechanical models of the knee joint allow the development of accurate procedures as well as novel devices to restore the joint natural motion. They are also used within musculoskeletal models to perform clinical gait analysis on patients. Among relevant knee models in the literature, the anatomy-based spatial parallel mechanisms represent the joint motion using rigid links for the ligaments’ isometric fibres and point contacts for the articular surfaces. To customize analyses, therapies and devices, there is the need to define subject-specific models, but relevant procedures and their accuracy are still questioned. A procedure is here proposed and validated to define a customized knee model based on a spatial parallel mechanism. Computed tomography, magnetic resonance and 3D-video-fluoroscopy were performed on a healthy volunteer to define the personalized model geometry. The model was then validated by comparing the measured and the replicated joint motion. The model showed mean absolute difference and standard deviations in translations and rotations, respectively of 0.98 ± 0.40 mm and 0.68 ± 0.29 ° for the tibia–femur motion, and of 0.77 ± 0.15 mm and 2.09 ± 0.69 ° for the patella–femur motion. These results show that accurate personalized spatial models of knee kinematics can be obtained from in-vivo imaging.

Towards Subject-Specific Strength Training Design through Predictive Use of Musculoskeletal Models

Lower extremity dysfunction is often associated with hip muscle strength deficiencies. Detailed knowledge of the muscle forces generated in the hip under specific external loading conditions enables specific structures to be trained. The aim of this study was to find the most effective movement type and loading direction to enable the training of specific parts of the hip muscles using a standing posture and a pulley system. In a novel approach to release the predictive power of musculoskeletal modelling techniques based on inverse dynamics, flexion/extension and ab-/adduction movements were virtually created. To demonstrate the effectiveness of this approach, three hip orientations and an external loading force that was systematically rotated around the body were simulated using a state-of-the art OpenSim model in order to establish ideal designs for training of the anterior and posterior parts of the M. gluteus medius (GM). The external force direction as well as the hip orientation greatly influenced the muscle forces in the different parts of the GM. No setting was found for simultaneous training of the anterior and posterior parts with a muscle force higher than 50% of the maximum. Importantly, this study has demonstrated the use of musculoskeletal models as an approach to predict muscle force variations for different strength and rehabilitation exercise variations.