Computational musculoskeletal models estimate the internal forces that produce human movement by representing the bones, muscles, and joints of the body. The accuracy of these simulations depends on how well the model reflects the anatomy of the individual. Current practice personalises models by linearly scaling a generic template. Scaling cannot accurately reproduce internal anatomical landmarks such as hip joint centres, resulting in deviations of 20-40mm from imaging-based ground truth.
This study develops a method to predict anatomical landmark positions of the lower limbs directly from external skin surface geometry. The proposed approach achieves a mean prediction error of 25.2mm across 18 lower-limb landmarks. These are of the same order as the hip joint centre deviations reported for linear scaling.
The results show that external surface geometry contains sufficient information to estimate these internal anatomical locations, and that the regression model captures this relationship across participants. Prediction accuracy is limited by the consistency of the input skin representation.
These findings show that anatomical landmarks relevant for musculoskeletal modelling can be estimated from skin geometry, providing a non-invasive approach to obtaining subject-specific anatomical information. This establishes a framework for exploring surface-based methods for musculoskeletal model personalisation. ... Read more

Abstract - Background Most widely used musculoskeletal models are predominantly based on male anatomy. This limits accurate biomechanical analysis in women, despite notable sex differences in occurrence of musculoskeletal pathologies. Aim This study aimed to develop a female musculoskeletal model of the lower extremity (YONI). The YONI is compared to the generic (male-based) model in simulation with a female participant. Methods The YONI model was developed in OpenSim Creator based on MR images. Comparisons were done between the YONI model and a scaled RAJAG model and a personalized model of using motion capture data in simulations. Results For comparison between YONI and RAJAG with another female subject, mean RMS error for the personalized model was 0.0096 m (SD = 0.0013), for the scaled RAJAG model 0.0247 m (SD = 0.0268) and for the scaled YONI model 0.0097 m (SD = 0.0010). Although SPM paired t-test showed significant differences for both YONI and RAJAG compared to the personalized model for all joint angles, the YONI model showed lower t-values compared to RAJAG. Joint moments reveal larger differences between YONI and scaled RAJAG models in the hip angles during the swing phase. Reserve moments were low in hip flexion and hip adduction, but higher in knee flexion and ankle flexion. Conclusion While observed kinematic and dynamic differences require cautious interpretation due to model limitations and data constraints, this work represents a crucial step toward the development of a female musculoskeletal model, essential for advancing biomechanical research and clinical applications for women. ... Read more

Despite the recognized impact of sex on biomechanics, research remains biased toward male anatomy, raising concerns about the validity of musculoskeletal (MSK) model predictions for females. This study investigated whether sex-specific bone geometry variations predict differences in the proportional volumes of the Gluteus Maximus (GMAX) and Rectus Femoris (RFEM). Using an MRI-based nnU-Net segmentation model trained in this thesis, bone metrics were extracted, and muscle volumes were normalized to derive proportional volumes for 16 young adults (9F/7M). The segmentation model demonstrated high accuracy (DSC: 0.926 for bones, 0.954 for muscles), revealing significant sexual dimorphism in bone geometry. Males exhibited greater femoral offsets and knee widths, while females had larger posterior pelvic widths and depths. %RFEM was significantly higher in males (p = 0.01), but %GMAX showed no sex-related differences. Regression analysis identified femoral offset and femur length as partial predictors of %RFEM (R^2 = 0.478), with pelvis-femur length weakly predicting %GMAX (R^2 = 0.151). However, the low predictive power suggests limitations in using bone metrics to estimate muscle volume proportions. These findings indicate that femoral dimorphism may partially explain sex-related %RFEM differences, but its role in %GMAX remains unclear. Future research integrating additional biomechanical factors could enhance sex-specific MSK modeling accuracy. ... Read more

At Basalt rehabilitation clinic The Hague, gait analysis is currently mostly observational. No objective measures are used and there is no systematic use of recordings. The aim of this research is to find and verify an accessible instrumented gait analysis method that could be implemented in the current care path at Basalt The Hague. The current instrumented gait analysis systems are marker-based. Recently more research has been done with regard to markerless systems, since markers bring along artefacts like the soft-tissue artefact. Markerless systems work with pose detection through artificial intelligence. A limitation of these pose detection systems, is that it is still in a research phase and did not make its way into the clinics yet. In a comparison of the accuracy of open-source pose estimation methods for measuring gait kinematics, OpenPose has been found most accurate.

For this research a program of requirements is made for implementation of instrumented gait analysis in the clinic at Basalt The Hague. The coordinates of anatomical landmarks obtained with OpenPose are processed to kinematic parameters. Besides, spatiotemporal parameters are looked into. A gait analysis set-up (consisting of tripods and mobile phone cameras) with OpenPose is proposed and tested to verify the accuracy. The OpenPose outcomes are compared with the the Simi motion capture system at Basalt Delft. By determining the Pearson correlation coefficients between OpenPose and Simi motion capture system, the kinematic parameters are verified. The mean standard deviations of the repeated recordings with OpenPose are used to determine the repeatability of OpenPose.

With this research is verified that OpenPose is repeatable and that kinematic parameters of the hip and knee are highly correlated to the standard method of instrumented gait analysis used at Basalt. Therefore can be concluded that OpenPose pose detection seems a promising method for determining kinematic and possibly also spatiotemporal parameters of gait. With follow-up research, especially clinical validation, and further development of the data processing, the first steps can be made towards implementation of accessible instrumented gait analysis in the current care path at Basalt The Hague, and possibly other locations and/or institutions. ... Read more