Subject-specific hip geometry and hip joint centre location affects calculated contact forces at the hip during gait

Musculoskeletal modeling and movement simulation for structural hip disorder research: A scoping review of methods, validation, and applications

Musculoskeletal modeling is a powerful tool to quantify biomechanical factors typically not feasible to measure in vivo, such as hip contact forces and deep muscle activations. While technological advancements in musculoskeletal modeling have increased accessibility, selecting the appropriate modeling approach for a specific research question, particularly when investigating pathological populations, has become more challenging. The purposes of this review were to summarize current modeling and simulation methods in structural hip disorder research, as well as evaluate model validation and study reproducibility. MEDLINE and Web of Science were searched to identify literature relating to the use of musculoskeletal models to investigate structural hip disorders (i.e., involving a bony abnormality of the pelvis, femur, or both). Forty-seven articles were included for analysis, which either compared multiple modeling methods or applied a single modeling workflow to answer a research question. Findings from studies comparing methods were summarized, such as the effect of generic versus patient-specific modeling techniques on model-estimated hip contact forces or muscle forces. The review also discussed limitations in validation practices, as only 11 of the included studies conducted a validation and used qualitative approaches only. Given the lack of information related to model validation, additional details regarding the development and validation of generic models were retrieved from references and modeling software documentation. To address the wide variability and under-reporting of data collection, data processing, and modeling methods highlighted in this review, we developed a template that researchers can complete and include as a table within the methodology section of their manuscripts. The use of this table will help increase transparency and reporting of essential details related to reproducibility and methods without being limited by word count restrictions. Overall, this review provides a comprehensive synthesis of modeling approaches that can help researchers make modeling decisions and evaluate existing literature.

Novel hybrid rigid-deformable fetal modeling for simulating the vaginal delivery within the second stage of labor

BACKGROUND AND OBJECTIVE

The fetal representation as a 3D articulated body plays an essential role to describe a realistic vaginal delivery simulation. However, the current computational solutions have been oversimplified. The objective of the present work was to develop and evaluate a novel hybrid rigid-deformable modeling approach for the fetal body and then simulate its interaction with surrounding fetal soft tissues and with other maternal pelvis soft tissues during the second stage of labor.

METHODS

CT scan data was used for 3D fetal skeleton reconstruction. Then, a novel hybrid rigid-deformable model of the fetal body was developed. This model was integrated into a maternal 3D pelvis model to simulate the vaginal delivery. Soft tissue deformation was simulated using our novel HyperMSM formulation. Magnetic resonance imaging during the second stage of labor was used to impose the trajectory of the fetus during the delivery.

RESULTS

Our hybrid rigid-deformable fetal model showed a potential capacity for simulating the movements of the fetus along with the deformation of the fetal soft tissues during the vaginal delivery. The deformation energy density observed in the simulation for the fetal head fell within the strain range of 3 % to 5 %, which is in good agreement with the literature data.

CONCLUSIONS

This study developed, for the first time, a hybrid rigid-deformation modeling of the fetal body and then performed a vaginal delivery simulation using MRI-driven kinematic data. This opens new avenues for describing more realistic behavior of the fetal body kinematics and deformation during the second stage of labor. As perspectives, the integration of the full skeleton body, especially the upper and lower limbs will be investigated. Then, the completed model will be integrated into our developed next-generation childbirth training simulator for vaginal delivery simulation and associated complication scenarios.

Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue?

BACKGROUND

This study investigated the most accurate method for estimating the hip joint center position in clinical 3D gait analysis for young individuals with high amounts of soft tissue. We compared position estimates of five regression-based and two functional methods to the hip joint center position obtained through 3D free-hand ultrasound.

METHODS

For this purpose, the data of 14 overweight or obese individuals with a mean age of 13.6 (SD 2.1 yrs) and a BMI of 36.5 (SD 7.1 kg/m2, range 26-52 kg/m2) who underwent standard clinical 3D gait analysis were used. The data of each participant were processed with five regression-based and two functional methods and compared to the hip joint center identified via 3D free-hand ultrasound.

FINDINGS

The absolute location errors to 3D free-hand ultrasound for each anatomical plane and the Euclidean distances served as outcomes next to their effects on gait variables. The data suggest that regression-based methods are preferable to functional methods in this population, as the latter demonstrated the highest variability in accuracy with large errors for some individuals.

INTERPRETATION

Based on our findings we recommend using the regression method presented by Hara et al. due to its superior overall accuracy of <9 mm on average in all planes and the lowest impact on kinematic and kinetic output variables. We do not recommend using the Harrington equations (single and multiple) in populations with high amounts of soft tissue as they require pelvic depth as input, which can be massively biased when a lot of soft tissue is present around the pelvis.

Accuracy of conventional motion capture in measuring hip joint center location and hip rotations during gait, squat, and step-up activities

Accurate measurements of hip joint kinematics are essential for improving our understanding of the effects of injury, disease, and surgical intervention on long-term hip joint health. This study assessed the accuracy of conventional motion capture (MoCap) for measuring hip joint center (HJC) location and hip joint angles during gait, squat, and step-up activities while using dynamic biplane radiography (DBR) as the reference standard. Twenty-four young adults performed six trials of treadmill walking, six body-weight squats, and six step-ups within a biplane radiography system. Synchronized biplane radiographs were collected at 50 images per second and MoCap was collected simultaneously at 100 images per second. Bone motion during each activity was determined by matching digitally reconstructed radiographs, created from subject-specific CT-based bone models, to the biplane radiographs using a validated registration process. Errors in estimating HJC location and hip angles using MoCap were quantified by the root mean squared error (RMSE) across all frames of available data. The MoCap error in estimating HJC location was larger during step-up (up to 89.3 mm) than during gait (up to 16.6 mm) or squat (up to 31.4 mm) in all three anatomic directions (all p < 0.001). RMSE in hip joint flexion (7.2°) and abduction (4.3°) during gait was less than during squat (23.8° and 8.9°) and step-up (20.1° and 10.6°) (all p < 0.01). Clinical analysis and computational models that rely on skin-mounted markers to estimate hip kinematics should be interpreted with caution, especially during activities that involve deeper hip flexion.

Influence of Skin Marker Positioning and Their Combinations on Hip Joint Center Estimation Using the Functional Method

Accurate estimation of hip joint center (HJC) position is crucial during gait analysis. HJC is obtained with predictive or functional methods. But in the functional method, there is no consensus on where to place the skin markers and which combination to use. The objective of this study was to analyze how different combinations of skin markers affect the estimation of HJC position relative to predictive methods. Forty-one healthy volunteers were included in this study; thirteen markers were placed on the pelvis and hip of each subject's lower limbs. Various marker combinations were used to determine the HJC position based on ten calibration movement trials, captured by a motion capture system. The estimated HJC position for each combination was evaluated by focusing on the range and standard deviation of the mean norm values of HJC and the mean X, Y, Z coordinates of HJC for each limb. The combinations that produced the best estimates incorporated the markers on the pelvis and on proximal and easily identifiable muscles, with results close to predictive methods. The combination that excluded the markers on the pelvis was not robust in estimating the HJC position.

Full-length radiograph based automatic musculoskeletal modeling using convolutional neural network

Full-length radiographs contain information from which many anatomical parameters of the pelvis, femur, and tibia may be derived, but only a few anatomical parameters are used for musculoskeletal modeling. This study aimed to develop a fully automatic algorithm to extract anatomical parameters from full-length radiograph to generate a musculoskeletal model that is more accurate than linear scaled one. A U-Net convolutional neural network was trained to segment the pelvis, femur, and tibia from the full-length radiograph. Eight anatomic parameters (six for length and width, two for angles) were automatically extracted from the bone segmentation masks and used to generate the musculoskeletal model. Sørensen-Dice coefficient was used to quantify the consistency of automatic bone segmentation masks with manually segmented labels. Maximum distance error, root mean square (RMS) distance error and Jaccard index (JI) were used to evaluate the geometric accuracy of the automatically generated pelvis, femur and tibia models versus CT bone models. Mean Sørensen-Dice coefficients for the pelvis, femur and tibia 2D segmentation masks were 0.9898, 0.9822 and 0.9786, respectively. The algorithm-driven bone models were closer to the 3D CT bone models than the scaled generic models in geometry, with significantly lower maximum distance error (28.3 % average decrease from 24.35 mm) and RMS distance error (28.9 % average decrease from 9.55 mm) and higher JI (17.2 % average increase from 0.46) (P < 0.001). The algorithm-driven musculoskeletal modeling (107.15 ± 10.24 s) was faster than the manual process (870.07 ± 44.79 s) for the same full-length radiograph. This algorithm provides a fully automatic way to generate a musculoskeletal model from full-length radiograph that achieves an approximately 30 % reduction in distance errors, which could enable personalized musculoskeletal simulation based on full-length radiograph for large scale OA populations.

How mechanics of individual muscle-tendon units define knee and ankle joint function in health and cerebral palsy-a narrative review

This study reviews the relationship between muscle-tendon biomechanics and joint function, with a particular focus on how cerebral palsy (CP) affects this relationship. In healthy individuals, muscle size is a critical determinant of strength, with muscle volume, cross-sectional area, and moment arm correlating with knee and ankle joint torque for different isometric/isokinetic contractions. However, in CP, impaired muscle growth contributes to joint pathophysiology even though only a limited number of studies have investigated the impact of deficits in muscle size on pathological joint function. As muscles are the primary factors determining joint torque, in this review two main approaches used for muscle force quantification are discussed. The direct quantification of individual muscle forces from their relevant tendons through intraoperative approaches holds a high potential for characterizing healthy and diseased muscles but poses challenges due to the invasive nature of the technique. On the other hand, musculoskeletal models, using an inverse dynamic approach, can predict muscle forces, but rely on several assumptions and have inherent limitations. Neither technique has become established in routine clinical practice. Nevertheless, identifying the relative contribution of each muscle to the overall joint moment would be key for diagnosis and formulating efficient treatment strategies for patients with CP. This review emphasizes the necessity of implementing the intraoperative approach into general surgical practice, particularly for joint correction operations in diverse patient groups. Obtaining in vivo data directly would enhance musculoskeletal models, providing more accurate force estimations. This integrated approach can improve the clinicians' decision-making process and advance treatment strategies by predicting changes at the muscle and joint levels before interventions, thus, holding the potential to significantly enhance clinical outcomes.

Shape-model scaling is more robust than linear scaling to marker placement error

When reconstructing bone geometry to calculate joint kinematics, shape-model scaling can be more accurate and repeatable than linear scaling given the same anatomical landmarks. This study perturbed anatomical landmarks from optical motion capture and determined the robustness of shape-model scaling to misplaced markers compared to a traditional approach of linear scaling. We hypothesised that shape-model scaling would be less susceptible to variance in marker positions compared to linear scaling. The positions of hip joint centres and femoral/tibial segment lengths across perturbations were compared to determine each scaling method's range of geometric variation. The standard deviation (SD) of the hip joint centre location from the shape model had a maximum of 1.4 mm, compared to 4.2 mm for linear scaling. Femoral and tibial segments displayed SD's of 5.4 mm and 5.2 mm when shape-model scaled, compared to 9.2 mm and 9.5 mm with linear scaling, respectively, thus supporting our hypothesis. Geometric constraints within a shape model provide robustness to marker misplacement providing potential improvements in repeatability and data exchange.

Estimation of two wear factors for total hip arthroplasty: A simulation study based on musculoskeletal modelling

BACKGROUND

Primary causes of surgical revision after total hip arthroplasty are polyethylene wear and implant loosening. These factors are particularly related to joint friction and thus patients' physical activity. Assessing implant wear over time according to patients' morphology and physical activity level is key to improve follow-up and patients' quality of life.

METHODS

An approach initially proposed for tibiofemoral prosthetic wear estimation was adapted to compute two wear factors (force-velocity, directional wear intensity) using a musculoskeletal model. It was applied on 17 participants with total hip arthroplasty to compute joint angular velocity, contact force, sliding velocity, and wear factors during common daily living activities.

FINDINGS

Differences were observed between gait, sitting down, and standing up tasks. An incremental increase of both global wear factors (time-integral) was observed during gait from slow to fast speeds (p ≤ 0.01). Interestingly, these two wear factors did not result in same trend for sitting down and standing up tasks. Compared to gait, one cycle of sitting down or standing up tends to induce higher friction-related wear but lower cross-shear-related wear. Depending on the wear factor, significant differences can be found between sitting down and gait at slow speed (p ≤ 0.05), and between sitting down (p ≤ 0.05) or standing up (p ≤ 0.05) and gait at fast speed. Furthermore, depending on the activity, wear can be fostered by joint contact force and/or sliding velocity.

INTERPRETATION

This study demonstrated the potential of wear estimation to highlight activities inducing a higher risk of implant wear after total hip arthroplasty from motion capture data.

Hip centre regression progression: Same equations, better numbers

Accurate estimation of the hip joint centre (HJC) location is critical for modelling the kinematics and kinetics of the lower limb. Regression equations are commonly used to predict the HJC from anatomical landmarks on the pelvis, such as those published by Tylkowski et al., Andriacchi et al., Bell et al., and Seidel et al. Using a population of 159 CT-segmented pelvises, we assessed the accuracy of these methods as originally reported, and refined their parameters based on our larger cohort. We found the Tylkowski, Bell, and Seidel methods had mean Euclidean errors of 22.5, 26.4, and 17.9 mm, respectively. With new parameters for each method 'back-calculated' from our pelvic population, each method's error was reduced by an average of 69 %, with mean absolute errors of 7.9, 6.6, and 5.9 mm, respectively. For all methods, error has been reduced to below 1 cm, well below published levels for pelvic landmark estimation methods. These results highlight the need to validate and re-calibrate joint centre prediction methods on large, representative datasets to account for natural morphological variations.

On the estimation of hip joint loads through musculoskeletal modeling

Noninvasive estimation of joint loads is still an open challenge in biomechanics. Although musculoskeletal modeling represents a solid resource, multiple improvements are still necessary to obtain accurate predictions of joint loads and to translate such potential into practical utility. The present study, focused on the hip joint, is aimed at reviewing the state-of-the-art literature on the estimation of hip joint reaction forces through musculoskeletal modeling. Our literature inspection, based on well-defined selection criteria, returned seventeen works, which were compared in terms of methods and results. Deviations between predicted and in vivo measured hip joint loads, taken from the OrthoLoad database, were assessed through quantitative deviation indices. Despite the numerous modeling and computational improvements made over the last two decades, predicted hip joint loads still deviate from their experimental counterparts and typically overestimate them. Several critical aspects have emerged that affect muscle force estimation, hence joint loads. Among them, the physical fidelity of the musculoskeletal model, with its parameters and geometry, plays a crucial role. Also, predicted joint loads are markedly affected by the selected muscle recruitment strategy, which reflects the underlying motor control policy. Practical guidelines for researchers interested in noninvasive estimation of hip joint loads are also provided.

The role of computational models in mechanobiology of growing bone

Endochondral ossification, the process by which long bones grow in length, is regulated by mechanical forces. Computational models, specifically finite element models, have been used for decades to understand the role of mechanical loading on endochondral ossification. This perspective outlines the stages of model development in which models are used to: 1) explore phenomena, 2) explain pathologies, 3) predict clinical outcomes, and 4) design therapies. As the models progress through the stages, they increase in specificity and biofidelity. We give specific examples of models of endochondral ossification and expect models of other mechanobiological systems to follow similar development stages.

Framework of sampling the subject-specific static loads from the gait cycle of interindividual variation

BACKGROUND AND OBJECTIVE

Numerous techniques for bone remodeling simulation have been developed based on Wolff's law. However, most studies have been conducted with empirically determined static loads, which cannot reflect subject-specific characteristics. We recently proposed a new concept of representative static loads (RSLs) to efficiently consider the effect of cyclically repeated dynamic loads on bone remodeling simulation. Based on this concept, the goal of this study is to sample the subject-specific static loads (SSL) from a general gait cycle of interindividual variation.

METHODS

A total of 15 gait cycles (ten normal and five abnormal cycles) obtained from the public database were used in this study. Each gait cycle was applied to a femur FE model constructed from the clinical CT scan data to evaluate the strain energy distribution as a reference. Then, a natural coordinate was introduced to maintain the predefined locations of extreme points (i.e., two peaks and one valley) for both normal and abnormal gait cycles. To determine the RSLs in the natural coordinate, five out of ten normal gait cycles were used. Through an inverse transformation for each gait cycle, the RSLs in the natural coordinate were converted to the SSLs in the original coordinate. Topology optimization results with the proposed SSLs were compared with those with a single full gait cycle (reference). For comparison, topology optimization was also conducted with empirically determined loads (EDLs) which have been widely used in the literature.

RESULTS

For normal gait cycles, the proposed SSLs reduced the average computing cost by 95.86% while suppressing the errors of bone mass distribution and apparent stiffness below maximum 4.24% and 1.72%, respectively. Even for abnormal gait cycles, the errors of bone mass distribution and apparent stiffness were suppressed below maximum 9.49% and 2.12%, respectively. Conversely, the conventional EDLs (peak loads selected in this study) showed significantly larger errors of maximum 47.28% and 30.31% in bone mass distribution and apparent stiffness for normal gait cycles.

CONCLUSION

By virtue of using the coordinate transformation for each gait cycle, the proposed SSLs achieved a higher accuracy in the bone mass distribution and apparent stiffness than the previous RSLs and EDLs. Furthermore, this approach can be used for abnormal gait cycles which have higher interindividual variation.

Predicting the hip joint centre in children: New regression equations, linear scaling, and statistical shape modelling

Determination of the hip joint centre (HJC) is important to accurately estimate hip joint motion, moments and muscle forces. The most accurate method for HJC estimation without medical imaging is an area of interest in the biomechanics community, especially in a paediatric population, which has not been widely evaluated. HJC locations were calculated by sphere-fitting to the acetabulum of three-dimensional pelvises segmented from 333 CT scans of children aged 4 to 18 years old. Three methods for determining the HJC were compared: regression equations, linear scaling, and shape model prediction. The new regression equations developed in this study produced Euclidean distance errors of 6.23 mm ± 2.90 mm. Linear scaling of paediatric bone produced errors of 3.90 mm ± 2.52 mm and adult bone scaling of 5.45 mm ± 3.26 mm. Prediction of the HJC using a paediatric statistical shape model produced mean Euclidian distance errors of 2.95 mm ± 1.65 mm. Overall, shape model prediction of the HJC produced the lowest errors, with linear scaling of a mean paediatric pelvis providing better estimates than regression equations.

Constitutional morphological features and risk of hip osteoarthritis: a case-control study using standard radiographs

OBJECTIVES

To evaluate the risk of association with hip osteoarthritis (OA) of 14 morphological features measured on standard antero-posterior pelvis radiographs.

METHODS

A case-control study of 566 symptomatic unilateral hip OA cases and 1108 controls without hip OA, using the Genetics of OA and Lifestyle database. Unaffected hips of cases were assumed to reflect pre-OA morphology of the contralateral affected hip. ORs with 95% CI adjusted for confounding factors were calculated using logistic regression. Hierarchical clustering on principal component method was used to identify clusters of morphological features. Proportional risk contribution (PRC) of these morphological features in the context of other risk factors of hip OA was estimated using receiver operating characteristic analysis.

RESULTS

All morphological features showed right-left symmetry in controls. Each feature was associated with hip OA after adjusting for age, gender and body mass index. Increased sourcil angle had the strongest association (OR: 6.93, 95% CI 5.16 to 9.32). Three clusters were identified. The PRC varied between individual features, as well as between clusters. It was 35% (95% CI 31% to 40%) for all 14 morphological features, compared to 21% (95% CI 19% to 24%) for all other well-established risk factors.

CONCLUSIONS

Constitutional morphological variation strongly associates with hip OA development and may explain much of its heritability. Relevant morphological measures can be assessed readily on standard radiographs to help predict risk of hip OA. Prospective studies are required to provide further support for causality.

The relationship between tibiofemoral geometry and musculoskeletal function during normal activity

BACKGROUND

The effect of tibiofemoral geometry on musculoskeletal function is important to movement biomechanics.

RESEARCH QUESTION

We hypothesised that tibiofemoral geometry determines tibiofemoral motion and musculoskeletal function. We then aimed at 1) modelling tibiofemoral motion during normal activity as a function of tibiofemoral geometry in healthy adults; and 2) quantifying the effect of tibiofemoral geometry on musculoskeletal function.

METHODS

We used motion data for six activity types and CT images of the knee from 12 healthy adults. Geometrical variation of the tibia and femoral articular surfaces were measured in the CT images. The geometry-based tibiofemoral motion was calculated by fitting a parallel mechanism to geometrical variation in the cohort. Matched musculoskeletal models embedding the geometry-based tibiofemoral joint motion and a common generic tibiofemoral motion of reference were generated and used to calculate joint angles, net joint moments, muscle and joint forces for the six activities analysed. The tibiofemoral model was validated against bi-planar fluoroscopy measurements for walking for all the six planes of motion. The effect of tibiofemoral geometry on musculoskeletal function was the difference between the geometry-based model and the model of reference.

RESULTS

The geometry-based tibiofemoral motion described the pattern and the variation during walking for all six motion components, except the pattern of anterior tibial translation. Tibiofemoral geometry had moderate effect on cohort-averages of musculoskeletal function (R² = 0.60-1), although its effect was high in specific instances of the model, outputs and activities analysed, reaching 2.94 BW for the ankle reaction force during stair descent. In conclusion, tibiofemoral geometry is a major determinant of tibiofemoral motion during walking.

SIGNIFICANCE

Geometrical variations of the tibiofemoral joint are important for studying musculoskeletal function during normal activity in specific individuals but not for studying cohort averages of musculoskeletal function. This finding expands current knowledge of movement biomechanics.

Lower functioning patients demonstrate atypical hip joint loading before and following total hip arthroplasty for osteoarthritis

Previous studies have established that up to 1 year post total hip arthroplasty (THA), patients do not recover normal function and the magnitude of hip joint loading remains reduced compared to healthy individuals. However, the temporal nature of the loading profile has not been considered to identify individuals who are at a greater risk of poor functional outcomes following THA. This study aimed to determine changes to the profile and magnitude of the resultant hip joint reaction force before and up to 6 months post-primary THA, and factors associated with atypical loading profiles. Hip joint loading was computed using a personalized lower-limb musculoskeletal model in 43 participants awaiting primary THA for osteoarthritis (mean age: SD = 65, 14 years; body mass index: SD = 30, 5 kg/m² ) before and up to 6 months after THA. Atypical, single-peak loading profiles were observed for 11 patients before surgery, where four showed a single peak at 6 months. Patients displaying a single-peak profile walked slower (mean difference: -0.4 m/s) compared to individuals displaying double-peak profile (P = <.001) and had significantly reduced sagittal plane hip range of motion during gait (mean difference -9.6°, P = <.001). Self-reported pain, function, and stiffness did not differentiate between patients with a single or double-peak loading profile. Individuals with a single-peak force profile did not meet the minimal clinically important hip range of motion during gait and would be classified as low-functioning THA patients. Clinical Relevance: The temporal nature of the force profile may help to identify individuals who are at the greatest risk of poor functional outcomes after THA.

Predicting longitudinal changes in joint contact forces in a juvenile population: scaled generic versus subject-specific musculoskeletal models

Subject-specific musculoskeletal model use in clinical settings is limited due to development-associated time and effort burdens together with potential medical imaging unavailability. As an alternative, this study investigated consistency in estimating longitudinal changes in joint contact forces (JCF) between scaled generic and subject-specific models. For 11 children, joint kinematics and JCF were calculated using subject-specific and scaled generic models. JCF changes estimated by both models were strongly correlated for the hip and knee although JCF estimates varied between models. Findings suggest that within specified limits of accuracy, scaled generic models are sensitive enough to detect JCF changes consistent with subject-specific models.

Minimal medical imaging can accurately reconstruct geometric bone models for musculoskeletal models

Accurate representation of subject-specific bone anatomy in lower-limb musculoskeletal models is important for human movement analyses and simulations. Mathematical methods can reconstruct geometric bone models using incomplete imaging of bone by morphing bone model templates, but the validity of these methods has not been fully explored. The purpose of this study was to determine the minimal imaging requirements for accurate reconstruction of geometric bone models. Complete geometric pelvis and femur models of 14 healthy adults were reconstructed from magnetic resonance imaging through segmentation. From each complete bone segmentation, three sets of incomplete segmentations (set 1 being the most incomplete) were created to test the effect of imaging incompleteness on reconstruction accuracy. Geometric bone models were reconstructed from complete sets, three incomplete sets, and two motion capture-based methods. Reconstructions from (in)complete sets were generated using statistical shape modelling, followed by host-mesh and local-mesh fitting through the Musculoskeletal Atlas Project Client. Reconstructions from motion capture-based methods used positional data from skin surface markers placed atop anatomic landmarks and estimated joint centre locations as target points for statistical shape modelling and linear scaling. Accuracy was evaluated with distance error (mm) and overlapping volume similarity (%) between complete bone segmentation and reconstructed bone models, and statistically compared using a repeated measure analysis of variance (p<0.05). Motion capture-based methods produced significantly higher distance error than reconstructions from (in)complete sets. Pelvis volume similarity reduced significantly with the level of incompleteness: complete set (92.70±1.92%), set 3 (85.41±1.99%), set 2 (81.22±3.03%), set 1 (62.30±6.17%), motion capture-based statistical shape modelling (41.18±9.54%), and motion capture-based linear scaling (26.80±7.19%). A similar trend was observed for femur volume similarity. Results indicate that imaging two relevant bone regions produces overlapping volume similarity >80% compared to complete segmented bone models, and improve analyses and simulation over current standard practice of linear scaling musculoskeletal models.

Musculoskeletal models with generic and subject-specific geometry estimate different joint biomechanics in dysplastic hips

Optimizing the geometric complexity of musculoskeletal models is important for reliable yet feasible estimation of joint biomechanics. This study investigated the effects of subject-specific model geometry on hip joint reaction forces (JRFs) and muscle forces in patients with developmental dysplasia of the hip (DDH) and healthy controls. For nine DDH and nine control subjects, three models were created with increasingly subject-specific pelvis geometry, hip joint center locations and muscle attachments. Hip JRFs and muscle forces during a gait cycle were compared among the models. For DDH subjects, resultant JRFs from highly specific models including subject-specific pelvis geometry, joint locations and muscle attachments were not significantly different compared to models using generic geometry in early stance, but were significantly higher in late stance (p = 0.03). Estimates from moderately specific models using CT-informed scaling of generic pelvis geometry were not significantly different from low specificity models using generic geometry scaled with skin markers. For controls, resultant JRFs in early stance from highly specific models were significantly lower than moderate and low specificity models (p ≤ 0.02) with no significant differences in late stance. Inter-model JRF differences were larger for DDH subjects than controls. Inter-model differences for JRF components and muscle forces were similar to resultant JRFs. Incorporating subject-specific pelvis geometry significantly affects JRF and muscle force estimates in both DDH and control groups, which may be especially important for reliable estimation of pathomechanics in dysplastic hips.

Long-term hip loading in unilateral total hip replacement patients is no different between limbs or compared to healthy controls at similar walking speeds

Variation in hip joint contact forces directly influences the performance of total hip replacements (THRs). Measurement and calculation of contact forces in THR patients has been limited by small sample sizes, wide variation in patient and surgical factors, and short-term follow-up. This study hypothesised that, at long-term follow-up, unilateral THR patients have similar calculated hip contact forces compared to controls walking at similar (self-selected) speeds and, in contrast, THR patients walking at slower (self-selected) speeds have reduced hip contact forces. It was further hypothesised that there is no difference in calculated hip contact forces between operated and non-operated limbs at long-term follow-up for both faster and slower patients. Gait analysis data for THR patients walking at faster (walking speed: 1.29 ± 0.12 m/s; n = 11) and slower (walking speed: 0.72 ± 0.09 m/s; n = 11) speeds were used. Healthy subjects constituted the control group (walking speed: 1.36 ± 0.12 m/s; n = 10). Hip contact forces were calculated using static optimisation. There was no significant difference (p > 0.31) in hip contact forces between faster and control groups. Conversely, force was reduced at heel strike by 19% (p = 0.002), toe-off by 31% (p < 0.001) and increased at mid-stance by 15% (p = 0.02) for the slower group compared to controls. There were no differences between operated and non-operated limbs for the slower group or the faster group, suggesting good biomechanical recovery at long-term follow-up. Loading, at different walking speeds, presented here can improve the relevance of preclinical testing methods.

Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction

Musculoskeletal models represent a powerful tool to gain knowledge on the internal forces acting at the joint level in a non-invasive way. However, these models can present some errors associated with the level of detail in their geometrical representation. For this reason, a thorough validation is necessary to prove the reliability of their predictions. This study documents the development of a generic musculoskeletal model and proposes a working logic and simulation techniques for identifying specific model features in need of refinement; as well as providing a quantitative validation for the prediction of hip contact forces (HCF). The model, implemented in the AnyBody Modeling System and based on the cadaveric dataset TLEM 2.0, was scaled to match the anthropometry of a patient fitted with an instrumented hip implant and to reproduce gait kinematics based on motion capture data. The relative contribution of individual muscle elements to the HCF and joint moments was analyzed to identify critical geometries, which were then compared to muscle magnetic resonance imaging (MRI) scans and, in case of inconsistencies, were modified to better match the volumetric scans. The predicted HCF showed good agreement with the overall trend and timing of the measured HCF from the instrumented prosthesis. The average root mean square error (RMSE), calculated for the total HCF was found to be 0.298*BW. Refining the geometries of the muscles thus identified reduced RMSE on HCF magnitudes by 17% (from 0.359*BW to 0.298*BW) over the whole gait cycle. The detailed study of individual muscle contributions to the HCF succeeded in identifying muscles with incorrect anatomy, which would have been difficult to intuitively identify otherwise. Despite a certain residual over-prediction of the final hip contact forces in the stance phase, a satisfactory level of geometrical accuracy of muscle paths has been achieved with the refinement of this model.

Sensitivity analysis of the estimated muscle forces during gait with respect to the musculoskeletal model parameters and dynamic simulation techniques

The purpose of the current study was to investigate the robustness of dynamic simulation results in the presence of uncertainties resulting from application of a scaled-generic musculoskeletal model instead of a subject-specific model as well as the effect of the choice of simulation method on the obtained muscle forces. The performed sensitivity analysis consisted of the following multibody parameter modifications: maximum isometric muscle forces, number of muscles, the hip joint centre location, segment masses as well as different dynamic simulation methods, namely static optimization with three different criteria and a computed muscle control algorithm (hybrid approach combining forward and inverse dynamics). Twenty-four different models and fifty-five resultant dynamic simulation data sets were analysed. The effects of model perturbation on the magnitude and profile of muscle forces were compared. It has been shown that estimated muscle forces are very sensitive to model parameters. The greatest impact was observed in the case of the force magnitude of the muscles generating high forces during gait (regardless of the modification introduced). However, the force profiles of those muscles were preserved. Relatively large differences in muscle forces were observed for different simulation techniques, which included both magnitude and profile of muscle forces. Personalization of model parameters would affect the resultant muscle forces and seems to be necessary to improve general accuracy of the estimated parameters. However, personalization alone will not ensure high accuracy due to the still unresolved muscle force sharing problem.

Hip- and patellofemoral-joint loading during gait are increased in children with idiopathic torsional deformities

BACKGROUND

Torsional deformities of the femur and tibia are associated with gait impairments and joint pain. Several studies have investigated these gait deviations in children with cerebral palsy. However, relatively little is known about gait deviations in children with idiopathic torsion and debate ensues about the management of these patients.

RESEARCH QUESTION

What are the effects of idiopathic increased femoral neck anteversion and external tibial torsion on lower-limb kinematics, kinetics and joint loading during gait in children and adolescents.

METHODS

Patient-specific musculoskeletal models were created for 12 children/adolescents (mean age of 14 years) with torsional deformities using low-dose biplane radiographic imaging and 3D gait analysis. Comparisons of joint motion and net joint torques during gait were made to an age-matched control group with no torsional deformities. The effects of torsional deformities on muscle and joint contact forces were investigated using two personalised musculoskeletal models: one with normal torsion and another with patient-specific torsion.

RESULTS

Femoral neck anteversion and external tibial torsion for the patients were (mean ± SD) 38° ± 9° and 40° ± 10°, respectively. Patients had increased internal hip rotation and external knee rotation as well as increased pelvic tilt during gait. Additionally, the efficacy of the plantarflexor-knee extension mechanism was diminished. Hip joint contact force was higher in the model with patient-specific torsion. The mediolateral component of the patellofemoral joint contact force was also increased despite the magnitude of the resultant patellofemoral contact force being unchanged.

SIGNIFICANCE

It has been previously established that idiopathic lower-limb torsional deformities alter gait kinematics. However, this study also showed that loading of the hip and patellofemoral joints are increased. This is an important insight for the clinical management of these patients and highlights that idiopathic lower-limb torsional deformities are not a purely cosmetic issue.

Force Closure Mechanism Modeling for Musculoskeletal Multibody Simulation

OBJECTIVE

Neuro-musculoskeletal multibody simulation (NMBS) seeks to optimize decision-making for patients with neuro-musculoskeletal disorders. In clinical practice, however, the inter-subject variability and the inaccessibility for experimental testing impede the reliable model identification. These limitations motivate the novel modeling approach termed as force closure mechanism modeling (FCM²).

METHODS

FCM ² expresses the dynamics between mutually articulating joint partners with respect to instantaneous screw axes (ISA) automatically reconstructed from their relative velocity state. Thereby, FCM² reduces arbitrary open-chain multibody topologies to force closure n-link pendulums. Within a computational validation study on the human knee joint with implemented contact surfaces, we examine FCM² as an underlying inverse dynamic model for computed muscle control. We evaluate predicted tibiofemoral joint quantities, i.e., kinematics and contact forces along with muscle moment arms, during muscle-induced knee motion against the classic hinge joint model and experimental studies.

RESULTS

Our NMBS study provided the proof-of-principle of the novel modeling approach. FCM² freed us from assuming a certain joint formulation while correctly predicting the joint dynamics in agreement with the established methods. Although experimental results were closely predicted, owing to noise in the ISA estimation, muscle moment arms were overestimated (R_ISA = 0.84 < R_HINGE = 0.97, RMSE_ISA = 13.18 mm > RMSE_HINGE = 6.54 mm), identifying the robust ISA estimation as key to FCM².

CONCLUSION

FCM² automatically derives the equations of motion in closed form. Moreover, it captures subject-specific joint function and, thereby, minimizes modeling and parameterization efforts.

SIGNIFICANCE

Model derivation becomes driven by quantitative data available in clinical settings so that FCM² yields a promising framework toward subject-specific NMBS.

Hip movement pathomechanics of patients with hip osteoarthritis aim at reducing hip joint loading on the osteoarthritic side

This study aims at defining gait pathomechanics in patients with hip osteoarthritis (OA) and their effect on hip joint loading by combining analyses of hip kinematics, kinetics and contact forces during gait. Twenty patients with hip OA and 17 healthy volunteers matched for age and BMI performed three-dimensional gait analysis. Hip OA level was evaluated based on plane radiographs using the Tönnis classification. Hip joint kinematics, kinetics as well as hip contact forces were calculated. Waveforms were time normalized and compared between groups using statistical parametric mapping analysis. Patients walked with reduced hip adduction angle and reduced hip abduction and external rotation moments. The work generated by the hip abductors during the stance phase of gait was largely decreased. These changes resulted in a decrease and a more vertical and anterior orientation of the hip contact forces compared to healthy controls. This study documents alterations in hip kinematics and kinetics resulting in decreased hip loading in patients with hip OA. The results suggested that patients altered their gait to increase medio-lateral stability, thereby decreasing demand on the hip abductors. These findings support discharge of abductor muscles that may bear clinical relevance of tailored rehabilitation targeting hip abductor muscles strengthening and gait retraining.

Bioinspired Technologies to Connect Musculoskeletal Mechanobiology to the Person for Training and Rehabilitation

Musculoskeletal tissues respond to optimal mechanical signals (e.g., strains) through anabolic adaptations, while mechanical signals above and below optimal levels cause tissue catabolism. If an individual's physical behavior could be altered to generate optimal mechanical signaling to musculoskeletal tissues, then targeted strengthening and/or repair would be possible. We propose new bioinspired technologies to provide real-time biofeedback of relevant mechanical signals to guide training and rehabilitation. In this review we provide a description of how wearable devices may be used in conjunction with computational rigid-body and continuum models of musculoskeletal tissues to produce real-time estimates of localized tissue stresses and strains. It is proposed that these bioinspired technologies will facilitate a new approach to physical training that promotes tissue strengthening and/or repair through optimal tissue loading.

Accuracy and Reliability of Marker-Based Approaches to Scale the Pelvis, Thigh, and Shank Segments in Musculoskeletal Models

Gait analysis together with musculoskeletal modeling is widely used for research. In the absence of medical images, surface marker locations are used to scale a generic model to the individual’s anthropometry. Studies evaluating the accuracy and reliability of different scaling approaches in a pediatric and/or clinical population have not yet been conducted and, therefore, formed the aim of this study. Magnetic resonance images (MRI) and motion capture data were collected from 12 participants with cerebral palsy and 6 typically developed participants. Accuracy was assessed by comparing the scaled model’s segment measures to the corresponding MRI measures, whereas reliability was assessed by comparing the model’s segments scaled with the experimental marker locations from the first and second motion capture session. The inclusion of joint centers into the scaling process significantly increased the accuracy of thigh and shank segment length estimates compared to scaling with markers alone. Pelvis scaling approaches which included the pelvis depth measure led to the highest errors compared to the MRI measures. Reliability was similar between scaling approaches with mean ICC of 0.97. The pelvis should be scaled using pelvic width and height and the thigh and shank segment should be scaled using the proximal and distal joint centers.

Methodological factors affecting joint moments estimation in clinical gait analysis: a systematic review

Quantitative gait analysis can provide a description of joint kinematics and dynamics, and it is recognized as a clinically useful tool for functional assessment, diagnosis and intervention planning. Clinically interpretable parameters are estimated from quantitative measures (i.e. ground reaction forces, skin marker trajectories, etc.) through biomechanical modelling. In particular, the estimation of joint moments during motion is grounded on several modelling assumptions: (1) body segmental and joint kinematics is derived from the trajectories of markers and by modelling the human body as a kinematic chain; (2) joint resultant (net) loads are, usually, derived from force plate measurements through a model of segmental dynamics. Therefore, both measurement errors and modelling assumptions can affect the results, to an extent that also depends on the characteristics of the motor task analysed (i.e. gait speed). Errors affecting the trajectories of joint centres, the orientation of joint functional axes, the joint angular velocities, the accuracy of inertial parameters and force measurements (concurring to the definition of the dynamic model), can weigh differently in the estimation of clinically interpretable joint moments. Numerous studies addressed all these methodological aspects separately, but a critical analysis of how these aspects may affect the clinical interpretation of joint dynamics is still missing. This article aims at filling this gap through a systematic review of the literature, conducted on Web of Science, Scopus and PubMed. The final objective is hence to provide clear take-home messages to guide laboratories in the estimation of joint moments for the clinical practice.

Predicting the location of the hip joint centres, impact of age group and sex

Clinical gait analysis incorporating three-dimensional motion analysis plays a key role in planning surgical treatments in people with gait disability. The position of the Hip Joint Centre (HJC) within the pelvis is thus critical to ensure accurate data interpretation. The position of the HJC is determined from regression equations based on anthropometric measurements derived from relatively small datasets. Current equations do not take sex or age into account, even though pelvis shape is known to differ between sex, and gait analysis is performed in populations with wide range of age. Three dimensional images of 157 deceased individuals (37 children, 120 skeletally matured) were collected with computed tomography. The location of the HJC within the pelvis was determined and regression equations to locate the HJC were developed using various anthropometrics predictors. We determined if accuracy improved when age and sex were introduced as variables. Statistical analysis did not support differentiating the equations according to sex. We found that age only modestly improved accuracy. We propose a range of new regression equations, derived from the largest dataset collected for this purpose to date.

In-vivo quantification of dynamic hip joint center errors and soft tissue artifact

Hip joint center (HJC) measurement error can adversely affect predictions from biomechanical models. Soft tissue artifact (STA) may exacerbate HJC errors during dynamic motions. We quantified HJC error and the effect of STA in 11 young, asymptomatic adults during six activities. Subjects were imaged simultaneously with reflective skin markers (SM) and dual fluoroscopy (DF), an x-ray based technique with submillimeter accuracy that does not suffer from STA. Five HJCs were defined from locations of SM using three predictive (i.e., based on regression) and two functional methods; these calculations were repeated using the DF solutions. Hip joint center motion was analyzed during six degrees-of-freedom (default) and three degrees-of-freedom hip joint kinematics. The position of the DF-measured femoral head center (FHC), served as the reference to calculate HJC error. The effect of STA was quantified with mean absolute deviation. HJC errors were (mean±SD) 16.6±8.4mm and 11.7±11.0mm using SM and DF solutions, respectively. HJC errors from SM measurements were all significantly different from the FHC in at least one anatomical direction during multiple activities. The mean absolute deviation of SM-based HJCs was 2.8±0.7mm, which was greater than that for the FHC (0.6±0.1mm), suggesting that STA caused approximately 2.2mm of spurious HJC motion. Constraining the hip joint to three degrees-of-freedom led to approximately 3.1mm of spurious HJC motion. Our results indicate that STA-induced motion of the HJC contributes to the overall error, but inaccuracies inherent with predictive and functional methods appear to be a larger source of error.

Calculation of muscle forces during normal gait under consideration of femoral bending moments

This paper introduces a new approach for computing lower extremity muscle forces by incorporating equations that consider "bone structure" and "prevention of bending by load reduction" into existing optimization algorithms. Lower extremity muscle and joint forces, during normal gait, were calculated and compared using two different optimization approaches. We added constraint equations that prevent femoral bending loads to an existing approach that considers "minimal total muscular force". Gait parameters such as kinematics, ground reaction forces, and surface electromyographic activation patterns were examined using standardized gait analysis. A subject-specific anatomic model of the lower extremities, obtained from magnetic resonance images of a healthy male, was used for the simulations. Finite element analysis was used to calculate femoral loads. The conventional method of calculating muscle forces leads to higher rates of femoral bending and structural stress than the new approach. Adding equations with structural subject-specific parameters in our new approach resulted in reduced femoral stress patterns. These findings show that our new approach improves the accuracy of femoral stress and strain simulations. Structural overloads caused by bending can be avoided during inverse calculation of muscle forces.

Muscle parameters estimation based on biplanar radiography

The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography.

Subject-specific geometrical detail rather than cost function formulation affects hip loading calculation

This study assessed the relative importance of introducing an increasing level of medical image-based subject-specific detail in bone and muscle geometry in the musculoskeletal model, on calculated hip contact forces during gait. These forces were compared to introducing minimization of hip contact forces in the optimization criterion. With an increasing level of subject-specific detail, specifically MRI-based geometry and wrapping surfaces representing the hip capsule, hip contact forces decreased and were more comparable to contact forces measured using instrumented prostheses (average difference of 0.69 BW at the first peak compared to 1.04 BW for the generic model). Inclusion of subject-specific wrapping surfaces in the model had a greater effect than altering the cost function definition.

Side-to-side variation in normal femoral morphology: 3D CT analysis of 122 femurs

BACKGROUND

The contralateral femur is often used as reference for reconstruction in unilateral hip joint pathology. The objective of this study was to quantify the side-to-side variation in proximal femur. We hypothesized that significant side-to-side differences exist between left and right femur with implications for preoperative planning and leg length discrepancy following hip arthroplasty.

MATERIALS AND METHODS

CT-based 3D femoral models were reconstructed for 122 paired femurs in 61 young healthy subjects (46.9±6.8 years) with no history of hip pathology. Side-to-side differences of several femoral morphologic parameters, including femoral head diameter, femoral anteversion, horizontal offset and femoral head center location, were compared and correlated with demographic factors using multiple linear regression.

RESULTS

Significant side-to-side differences (P<0.01) were found in femoral anteversion (4.3±3.8°; range: 0.2° to 17.3°), horizontal offset (2.5±2.1mm; range: 0.1 to 10.3mm), and femoral head center location (7.1±3.8mm; range: 0.5 to 19.4mm). The difference in femoral anteversion was strongly correlated with the difference in neck diameter (R(2)=0.79), whereas the difference in horizontal femoral offset was highly correlated with the head diameter difference (R(2)=0.72). Femoral head center difference was correlated with the femoral anteversion, horizontal offset and neck-shaft-angle difference (R(2)=0.82).

DISCUSSION

Relying on the anatomic landmarks of the contralateral femur during hip arthroplasty may not necessarily result in restoration of native anatomy and leg-length. Knowledge of the baseline side-to-side asymmetry could provide a range of error that would be tolerable following hip reconstruction.

LEVEL OF EVIDENCE

Level IV.

TYPE OF STUDY

Retrospective observational study.

Hip Joint Stresses Due to Cam-Type Femoroacetabular Impingement: A Systematic Review of Finite Element Simulations

Background

The cam deformity causes the anterosuperior femoral head to obstruct with the acetabulum, resulting in femoroacetabular impingement (FAI) and elevated risks of early osteoarthritis. Several finite element models have simulated adverse loading conditions due to cam FAI, to better understand the relationship between mechanical stresses and cartilage degeneration. Our purpose was to conduct a systematic review and examine the previous finite element models and simulations that examined hip joint stresses due to cam FAI.

Methods

The systematic review was conducted to identify those finite element studies of cam-type FAI. The review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and studies that reported hip joint contact pressures or stresses were included in the quantitative synthesis.

Results

Nine articles studied FAI morphologies using finite element methods and were included in the qualitative synthesis. Four articles specifically examined contact pressures and stresses due to cam FAI and were included in the quantitative synthesis. The studies demonstrated that cam FAI resulted in substantially elevated contact pressures (median = 10.4 MPa, range = 8.5–12.2 MPa) and von Mises stresses (median 15.5 MPa, range = 15.0–16.0 MPa) at the acetabular cartilage; and elevated maximum-shear stress on the bone (median = 15.2 MPa, range = 14.3–16.0 MPa), in comparison with control hips, during large amplitudes of hip motions. Many studies implemented or adapted idealized, ball-and-cup, parametric models to predict stresses, along with homogeneous bone material properties and in vivo instrumented prostheses loading data.

Conclusion

The formulation of a robust subject-specific FE model, to delineate the pathomechanisms of FAI, remains an ongoing challenge. The available literature provides clear insight into the estimated stresses due to the cam deformity and provides an assessment of its risks leading to early joint degeneration.

A Novel Approach for Dynamic Testing of Total Hip Dislocation under Physiological Conditions

Constant high rates of dislocation-related complications of total hip replacements (THRs) show that contributing factors like implant position and design, soft tissue condition and dynamics of physiological motions have not yet been fully understood. As in vivo measurements of excessive motions are not possible due to ethical objections, a comprehensive approach is proposed which is capable of testing THR stability under dynamic, reproducible and physiological conditions. The approach is based on a hardware-in-the-loop (HiL) simulation where a robotic physical setup interacts with a computational musculoskeletal model based on inverse dynamics. A major objective of this work was the validation of the HiL test system against in vivo data derived from patients with instrumented THRs. Moreover, the impact of certain test conditions, such as joint lubrication, implant position, load level in terms of body mass and removal of muscle structures, was evaluated within several HiL simulations. The outcomes for a normal sitting down and standing up maneuver revealed good agreement in trend and magnitude compared with in vivo measured hip joint forces. For a deep maneuver with femoral adduction, lubrication was shown to cause less friction torques than under dry conditions. Similarly, it could be demonstrated that less cup anteversion and inclination lead to earlier impingement in flexion motion including pelvic tilt for selected combinations of cup and stem positions. Reducing body mass did not influence impingement-free range of motion and dislocation behavior; however, higher resisting torques were observed under higher loads. Muscle removal emulating a posterior surgical approach indicated alterations in THR loading and the instability process in contrast to a reference case with intact musculature. Based on the presented data, it can be concluded that the HiL test system is able to reproduce comparable joint dynamics as present in THR patients.

Accuracy of Functional and Predictive Methods to Calculate the Hip Joint Center in Young Non-pathologic Asymptomatic Adults with Dual Fluoroscopy as a Reference Standard

Predictions from biomechanical models of gait may be sensitive to joint center locations. Most often, the hip joint center (HJC) is derived from locations of reflective markers adhered to the skin. Here, predictive techniques use regression equations of pelvic anatomy to estimate the HJC, whereas functional methods track motion of markers placed at the pelvis and femur during a coordinated motion. Skin motion artifact may introduce errors in the estimate of HJC for both techniques. Quantifying the accuracy of these methods is an area of open investigation. In this study, we used dual fluoroscopy (DF) (a dynamic X-ray imaging technique) and three-dimensional reconstructions from computed tomography images, to measure HJC locations in vivo. Using dual fluoroscopy as the reference standard, we then assessed the accuracy of three predictive and two functional methods. Eleven non-pathologic subjects were imaged with DF and reflective skin marker motion capture. Additionally, DF-based solutions generated virtual markers placed on bony landmarks, which were input to the predictive and functional methods to determine if estimates of the HJC improved. Using skin markers, functional methods had better mean agreement with the HJC measured by DF (11.0 ± 3.3 mm) than predictive methods (18.1 ± 9.5 mm); estimates from functional and predictive methods improved when using the DF-based solutions (1.3 ± 0.9 and 17.5 ± 8.6 mm, respectively). The Harrington method was the best predictive technique using both skin markers (13.2 ± 6.5 mm) and DF-based solutions (10.6 ± 2.5 mm). The two functional methods had similar accuracy using skin makers (11.1 ± 3.6 and 10.8 ± 3.2 mm) and DF-based solutions (1.2 ± 0.8 and 1.4 ± 1.0 mm). Overall, functional methods were superior to predictive methods for HJC estimation. However, the improvements observed when using the DF-based solutions suggest that skin motion artifact is a large source of error for the functional methods.

Effect of lower-limb joint models on subject-specific musculoskeletal models and simulations of daily motor activities

Understanding the validity of using musculoskeletal models is critical, making important to assess how model parameters affect predictions. In particular, assumptions on joint models can affect predictions from simulations of movement, and the identification of image-based joints is unavoidably affected by uncertainty that can decrease the benefits of increasing model complexity. We evaluated the effect of different lower-limb joint models on muscle and joint contact forces during four motor tasks, and assessed the sensitivity to the uncertainties in the identification of anatomical four-bar-linkage joints. Three MRI-based musculoskeletal models having different knee and ankle joint models were created and used for the purpose. Model predictions were compared against a baseline model including simpler and widely-adopted joints. In addition, a probabilistic analysis was performed by perturbing four-bar-linkage joint parameters according to their uncertainty. The differences between models depended on the motor task analyzed, and there could be marked differences at peak loading (up to 2.40 BW at the knee and 1.54 BW at the ankle), although they were rather small over the motor task cycles (up to 0.59 BW at the knee and 0.31 BW at the ankle). The model including more degrees of freedom showed more discrepancies in predicted muscle activations compared to measured muscle activity. Further, including image-based four-bar-linkages was robust to simulate walking, chair rise and stair ascent, but not stair descent (peak standard deviation of 2.66 BW), suggesting that joint model complexity should be set according to the imaging dataset available and the intended application, performing sensitivity analyses.