Musculoskeletal model choice influences hip joint load estimations during gait

Understanding the role of head size and neck length in micromotion generation at the taper junction in total hip arthroplasty

Modular hip implants allow intra-operative adjustments for patient-specific customization and targeted replacement of damaged elements without full implant extraction. However, challenges arise from relative micromotions between components, potentially leading to implant failure due to cytotoxic metal debris. In this study magnitude and directions of micromotions at the taper junction were estimated, aiming to understand the effect of variations in head size and neck length. Starting from a reference configuration adhering to the 12/14 taper standard, six additional implant configurations were generated by varying the head size and/or neck length. A musculoskeletal multibody model of a prothesized lower limb was developed to estimate hip contact force and location during a normal walking task. Following the implant assembly, the multibody-derived loads were imposed as boundary conditions in a finite element analysis to compute the taper junction micromotions as the relative slip between the contacting surfaces. Results highlighted the L-size head as the most critical configuration, indicating a 2.81 μm relative slip at the mid-stance phase. The proposed approach enables the investigation of geometric variations in implants under accurate load conditions, providing valuable insights for designing less risky prostheses and informing clinical decision-making processes.

Comparison of biomechanical analysis results using different musculoskeletal models for children with cerebral palsy

Introduction: Musculoskeletal model-based simulations have gained popularity as a tool for analyzing human movement biomechanics. However, when examining the same gait, different models with varying anatomical data and assumptions may produce inconsistent biomechanical results. This inconsistency is particularly relevant for children with cerebral palsy, who often exhibit multiple pathological gait patterns that can impact model outputs. Methods: The aim of this study was to investigate the effect of selecting musculoskeletal models on the biomechanical analysis results in children with cerebral palsy. Gait data were collected from multiple participants at slow, medium, and fast velocities. Joint kinematics, joint dynamics, and muscle activation were calculated using six popular musculoskeletal models within a biomechanical simulation environment. Results: The degree of inconsistency, measured as the root-mean-square deviation, in kinematic and kinetic results produced by the different models ranged from 4% to 40% joint motion range and 0%-28% joint moment range, respectively. The correlation between the results of the different models (both kinematic and kinetic) was good (R> 0.85, P < 0.01), with a stronger correlation observed in the kinetic results. Four of the six models showed a positive correlation between the simulated muscle activation of rectus femoris and the surface EMG, while all models exhibited a positive correlation between the activation of medial gastrocnemius and the surface EMG (P < 0.01). Discussion: These results provide insights into the consistency of model results, factors influencing consistency, characteristics of each model's outputs, mechanisms underlying these characteristics, and feasible applications for each model. By elucidating the impact of model selection on biomechanical analysis outcomes, this study advances the field's understanding of musculoskeletal modeling and its implications for clinical gait analysis model decision-making in children with cerebral palsy.

A Shoulder Musculoskeletal Model with Three-Dimensional Complex Muscle Geometries

Muscle structure is an essential component in typical computational models of the musculoskeletal system. Almost all musculoskeletal models represent muscle geometry using a set of line segments. The straight-line approach limits models' ability to accurately predict the paths of muscles with complex geometry. This approach needs knowledge of how the muscle changes shape and interacts with fundamental structures like muscles, bones, and joints that move. Moreover, the moment arms are supposed to be equivalent to all the fibers in the muscle. This study aims to create a shoulder musculoskeletal model that includes complex muscle geometries. We reconstructed the shape of fibers in the entire volume of six muscles adjacent to the shoulder using an automated technique. This method generates many fibers from the surface geometry of the skeletal muscle and its attachment areas. Highly discretized muscle representations for all muscles were created and used to simulate different shoulder movements. The moment arms of each muscle were calculated and validated against cadaveric measurements and models of the same muscles from the literature. We found that simulations using the developed musculoskeletal models generated more realistic geometries, which expands the physical representation of muscles compared to line segments. The shoulder musculoskeletal model with complex muscle geometry is created to increase the anatomical reality of models and the lines action of muscle fibers, and to be used for finite element investigations.

Patella height influences patellofemoral contact and kinematics following cruciate-retaining total knee replacement

The role of patella height is discussed controversially in total knee arthroplasty (TKA). Therefore, this computational study aims to systematically analyze the biomechanical effect of different patella heights on patellofemoral (PF) forces and kinematics after cruciate-retaining (CR) TKA. We implemented a CR bicondylar TKA with a dome patellar button in a validated dynamic musculoskeletal multibody model of a male human knee joint. Retropatellar dynamics (contact force [N], shear force [N], patellar shift [mm], tilt [°], and rotation [°]) were evaluated during dual-limb squat motion (flexion from 0° to 90°) with simulated active muscle forces and the effects of different patella heights (Blackburne-Peel [BP] ratio of 0.39, 0.49, 0.65, 0.85, 1.01, and 1.1 were systematically examined). As active knee flexion increased, PF contact force also increased. Patella alta (BP = 1.1) resulted in higher PF contact forces compared to normal patella height (BP = 0.65) by up to 16%. Contrarily, patella baja was associated with decreased PF forces by 7%. Compared to patella baja (BP = 0.39), patella alta (BP = 1.1) considerably increased the contact force by up to 25%. Different patellar heights mainly affected PF shear forces during early knee flexion. Concerning PF kinematics, patella alta (BP = 1.1) yielded a greater lateral tilt of more than 4° and higher patellar rotation by up to 3° during deep knee flexion, compared to normal patella height (BP = 0.65). Our computational study indicates that patella alta is associated with the highest PF contact and shear force after the implantation of a CR bicondylar TKA. This should be considered in PF disorders following TKA.

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.

Prediction of in vivo hip contact forces during common activities of daily living using a segment-based musculoskeletal model

Background: Quantifying in vivo hip muscle and contact forces during activities of daily living (ADL) provides valuable information for diagnosis and treatment of hip-related disorders. The objective of this study was to utilize Freebody, a segment-based musculoskeletal model, for the prediction of hip contact forces using a novel objective function during seven common ADLs and validate its performance against the publicly available HIP98 dataset. Methods: Marker data, ground reaction forces, and hip contact forces during slow, normal, and fast walking, stair ascent and descent, and standing up and sitting down were extracted for 3 subjects from the HIP98 dataset. A musculoskeletal anatomical dataset was scaled to match the dimensions of each subject, and muscle and hip contact forces were estimated by minimizing a novel objective function, which was the summation of the muscle stresses squared and body weight-normalised hip contact force. The accuracy of predictions were quantified using several metrics, and muscle forces were qualitatively compared to experimental EMGs in the literature. Results: FreeBody predicted the hip contact forces during the ADLs with encouraging accuracy: The root mean squared error of predictions were 44.0 ± 8.5, 47.4 ± 6.5, and 59.8 ± 7.1% BW during slow, normal, and fast walking, 44.2 ± 16.8% and 53.3 ± 12.2% BW for stair ascent and descent, and 31.8 ± 8.2% and 17.1 ± 5.0% BW for standing up and sitting down, respectively. The error in prediction of peak hip contact forces were 14-18%, 24-28%, 17-35% for slow, normal, and fast walking, 7-25% and 15-32% in stair ascent and descent, and around 10% for standing up and sitting down. The coefficient of determination was larger than 0.90 in all activities except in standing up (0.86 ± 0.08). Conclusion: This study has implemented a novel objective function in a segment-based musculoskeletal model, FreeBody, for the prediction of hip contact forces during a large range of ADLs. The model outputs compare favourably for all ADLs and are the best in standing up and sitting down, while muscle activation patterns are consistent with experimental EMGs from literature. This new objective function addresses one of the major limitations associated with musculoskeletal models in the literature, namely the high non-physiological predicted hip joint contact forces.

A Musculoskeletal Multibody Algorithm Based On a Novel Rheonomic Constraints Definition Applied to the Lower Limb

In this paper a multibody model was developed in the framework of biotribology of lower limb artificial joints. The presented algorithm performs the inverse dynamics of musculoskeletal systems with the aim to achieve a tool for the calculation of the joint reaction forces. The revolute joint, the cam joint, the spherical joint and the free joint were considered in the analysed lower limb system by introducing a novel analytical formulation of the rheonomic constraint equations based on the quaternions theory. Within the kinematical analysis the curved muscle paths were modelled by simulating their geodesic wrapping over bony surfaces and the muscle actuation states were formulated through the Hill muscle model. The developed analytical model written in Matlab environment allowed to follow the classical musculoskeletal analysis pipeline (kinematical analysis, inverse dynamics and static optimization) applied to the lower limb during the gait kinematics. The objective was to compare the calculated hip joint reactions with the ones obtained in vivo by Bergmann which are established as a reference input for total hip replacements lubrication models, in order to assume the developed algorithm as a fully open and controllable synovial joint tribological configuration generator tool, useful to be coupled with numerical lubrication/contact models in the framework of the artificial joints in silico wear analysis and prediction.

Dependency of lower limb joint reaction forces on femoral version

BACKGROUND

Musculoskeletal (MSK) models based on literature data are meant to represent a generic anatomy and are a popular tool employed by biomechanists to estimate the internal loads occurring in the lower limb joints, such as joint reaction forces (JRFs). However, since these models are normally just linearly scaled to an individual's anthropometry, it is unclear how their estimations would be affected by the personalization of key features of the MSK anatomy, one of which is the femoral version angle.

RESEARCH QUESTION

How are the lower limb JRF magnitudes computed through a generic MSK model affected by changes in the femoral version?

METHODS

We developed a bone-deformation tool in MATLAB (shared at https://simtk.org/projects/bone_deformity) and used it to create a set of seven OpenSim models spanning from 2˚ femoral retroversion to 40˚ anteversion. We used these models to simulate the gait of an elderly individual with an instrumented prosthesis implanted at their knee joint (5th Grand Challenge dataset) and quantified both the changes in JRFs magnitude due to varying the skeletal anatomy and their accuracy against the correspondent in vivo measurements at the knee joint.

RESULTS

Hip and knee JRF magnitudes were affected by the femoral version with variations from the unmodified generic model up to 17.9 ± 4.5% at the hip and 43.4 ± 27.1% at the knee joint. The ankle joint was unaffected by the femoral geometry. The MSK models providing the most accurate knee JRFs (root mean squared error: 0.370 ± 0.068 body weight, coefficient of determination: 0.757 ± 0.104, peak error range: 0.09-0.42 body weight) were those with femoral anteversion angle closer to that measured on the segmented bone of the individual.

SIGNIFICANCE

Femoral version substantially affects hip and knee JRFs estimated with generic MSK models, suggesting that personalizing key MSK anatomical features might be necessary for accurate estimation of JRFs with these models.

Effect of the underlying cadaver data and patient-specific adaptation of the femur and pelvis on the prediction of the hip joint force estimated using static models

The prediction of the hip joint force (HJF) is a fundamental factor for the prevention of edge loading in total hip arthroplasty. Naturally, the loading of the liner of the acetabular component depends on the HJF acting on the artificial joint. In contrast to dynamic musculoskeletal models, static models for HJF prediction do not require motion analysis of the patient. However, patient-specific adaptability and validity of static models have to be scrutinized. In this study, a modular framework for HJF prediction using static models is introduced to compare the results of different cadaver templates that are the basis of most static and dynamic models, and different scaling laws for the patient-specific adaptation with in vivo HJF of ten patients for one-leg stance and level walking. The results revealed the significant effect of the underlying cadaver template used for the prediction of the HJF (p < 0.01). A higher degree of patient-specific scaling of the cadaver template often did not significantly reduce the prediction error. Three static models with the lowest prediction errors were compared to results of dynamic models from literature. The prediction error of the peak HJF of the static models (median absolute errors below 15% body weight in magnitude and below 5° in direction) was similar in magnitude and even smaller in direction compared to dynamic models. The necessary reduction of a load-based target zone for the prevention of edge loading due to the uncertainty of the HJF prediction has to be considered in the preoperative planning. The framework for HJF prediction is openly accessible at https://github.com/RWTHmediTEC/HipJointForceModel.

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.

Automated Generation of Three-Dimensional Complex Muscle Geometries for Use in Personalised Musculoskeletal Models

The geometrical representation of muscles in computational models of the musculoskeletal system typically consists of a series of line segments. These muscle anatomies are based on measurements from a limited number of cadaveric studies that recently have been used as atlases for creating subject-specific models from medical images, so potentially restricting the options for personalisation and assessment of muscle geometrical models. To overcome this methodological limitation, we propose a novel, completely automated technique that, from a surface geometry of a skeletal muscle and its attachment areas, can generate an arbitrary number of lines of action (fibres) composed by a user-defined number of straight-line segments. These fibres can be included in standard musculoskeletal models and used in biomechanical simulations. This methodology was applied to the surfaces of four muscles surrounding the hip joint (iliacus, psoas, gluteus maximus and gluteus medius), segmented on magnetic resonance imaging scans from a cadaveric dataset, for which highly discretised muscle representations were created and used to simulate functional tasks. The fibres’ moment arms were validated against measurements and models of the same muscles from the literature with promising outcomes. The proposed approach is expected to improve the anatomical representation of skeletal muscles in personalised biomechanical models and finite element applications.